This paper presents a new method that improves robustness of real-coded Genetic Algorithm (GA) for function optimization. It is reported that most of crossover operators for real-coded GA have sampling bias, which prevents to find the optimum when it is near the boundary of search space. They like to search the center of search space much more than the other. Therefore, they will not work on functions that have their optima near the (...) boundary of the search space. Although several methods have been proposed to relax this sampling bias, they could not cancel whole bias. In this paper, we propose a new method, Toroidal Search Space Conversion (TSC), to remove this sampling bias. TSC converts bounded search space into toroidal one without any parameter. Experimental results show that a GA with TSC has higher performance to find the optimum near the boundary of search space and the GA has more robustness concerning the relative position of the optimum. (shrink)

With the advent of the cloud computing model allowing a shared access to massive computing facilities, a surging demand emerges for the protection of the intellectual property tied to the programs executed on these uncontrolled systems. If novel paradigm as confidential computing aims at protecting the data manipulated during the execution, obfuscating techniques (in particular at the source code level) remain a popular solution to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering (...) on the program even with the help of computing resources. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. Three program complexity metrics are used to define three different single-objective combinatorial optimization versions of the problem, which are solved and analysed. Additionally, three multi-objective problems are defined, those considering each of the selected metrics together with the program execution time, in order to avoid strong obfuscations penalizing the performance. The goal of the defined problems is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo-assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Classical genetic algorithms (GAs) are used to solve the studied problems, namely a basic cellular GA for the single-objective problems and the popular NSGA-II for the multi-objective ones. The promising results show the potential of the proposed technique. (shrink)

We propose a new Real-coded GA(RCGA) using the combination of two crossovers, UNDX-m and EDX. The search region of UNDX-m is biased to the inside area that the population of the RCGA covers. Because of this search bias, the GA using UNDX-m causes stagnation of its search if the cost function has a kind of structure, so called, a ridge structure or a multiple-peak structure. In order to overcome this stagnation, we propose a new crossover EDX, whose (...) search is biased toward extrapolative one. Experimental results show that RCGA with EDX can deal with both ridge-structure function whose dimension reaches more than hundreds and multiple-peak function whose optimum resides at the corner of the search area. (shrink)

Biologically inspired Evolution Algorithms, that use individuals as searching points and progress search by evolutions or adaptations of the individuals, are widely applied to many optimization problems. Many real world problems, which could be transformed to optimization problems, are very often difficult because the problems have complex landscapes that are multimodal, epistatic and having strong local minima. Current real-coded genetic algorithms could solve high-dimensional multimodal functions, but could not solve strong deceptive functions. (...) Niching GAs are applied to low-dimensional multimodal functions by maintaining diversity of searching population, but could not be applicable to highdimensional functions. In order to optimize high dimensional deceptive multimodal functions, we propose a new EA called Adaptive Neighboring Search, that is structured with a selection for reproduction by restricting mating individuals to neighbors, a crossover-like mutation using the mating individuals and an elitist selection for survival within one centered parent and its offsprings. By individualized generation alternation and complementary crossover-like mutation, the ANS realizes self-distributive and locally adaptive search, and individuals in the search divide into plural promising valleys and converge within same valley. The ANS is applicable to high dimensional deceptive multimodal function optimization, because the feature is independent of number of problem’s dimensions. By applying to high dimensional Fletcher and Powell function as a deceptive multimodal one, we show the ANS can obtain various solutions and several optimal solutions in high probability. (shrink)

For genetic algorithms, it is important to maintain the population diversity. Some genetic algorithms have been proposed, which have an ability to control the diversity. But these algorithms use the distance between two individuals to control the diversity. Therefore, these performances become worse on ill-scaled functions. In this paper, we propose a new genetic algorithm, DIDC(a genetic algorithm with Distance Independent Diversity Control), that does not use a distance to control the population diversity. For controlling (...) the diversity, DIDC uses two GAs that have different natures. For realizing different natures, one GA uses a crossover operator as a search operator, and the other GA uses a mutation operator in DIDC. By applying DIDC to several benchmark problems, we show that DIDC has a good performance on high dimensional, multimodal, non-separable and ill-scaled problems. Finally, we show that the control parameter of DIDC has the same effect on the search with the number of generating children n c. (shrink)

In this paper, we propose an extension of the Minimal Generation Gap (MGG) to reduce the number of fitness evaluation for the real-coded GAs (RCGA). When MGG is applied to actual engineering problems, for example applied to optimization of design parameters, the fitness calculating time is usually huge because MGG generates many children from one pair of parents and the fitness is calculated by repetitive simulation or analysis. The proposed method called Saving MGG reduces the number of fitness (...) evaluation by estimating the promising degrees of children using individual distribution and fitness information of population, and selecting children based on the promising degree before evaluating the fitness. Experimental results show that RCGA with Saving MGG can provide large reducing effects on 20 or 30 dimensional Sphere functions, Rosenbrock functions, ill-scaled Rosenbrock functions, and Rastrigin function. (shrink)

This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem (...) will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results. (shrink)

This paper presents a kernel density estimation method by means of real-coded crossovers. Functions of real-coded crossover operators are composed of probabilistic density estimation from parental populations and sampling from estimated models. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate probabilistic distributions, however, probabilistic model estimation is implicitly included in algorithms of real-coded crossovers. Based on this understanding, we exploit the implicit estimation of probabilistic distribution of crossovers as a (...) kernel density estimator. We also propose an application of crossover kernels to Expectation-Maximization estimation (EM) of Gaussian mixtures. (shrink)

We demonstrate a method for optimizing desired functionality in real complex chemical systems, using a genetic algorithm. The chemical systems studied here are mixtures of amphiphiles, which spontaneously exhibit a complex variety of self-assembled molecular aggregations, and the property optimized is turbidity. We also experimentally resolve the fitness landscape in some hyper-planes through the space of possible amphiphile formulations, in order to assess the practicality of our optimization method. Our method shows clear and significant progress after testing only (...) 1 % of the possible amphiphile formulations. (shrink)

In this paper, we perform theoretical analysis and experiments on the Simplex Crossover (SPX), which we have proposed. Real-coded GAs are expected to be a powerful function optimization technique for real-world applications where it is often hard to formulate the objective function. However, we believe there are two problems which will make such applications difficult; 1) performance of real-coded GAs depends on the coordinate system used to express the objective function, and 2) (...) it costs much labor to adjust parameters so that the GAs always find an optimum point efficiently. The result of our theoretical analysis and experiments shows that a performance of SPX is independent of linear coordinate transformation and that SPX always optimizes various test function efficiently when theoretical value for expansion rate, which is a parameter of SPX, is applied. We also show that BLX-α is equivalent to degenerate form of SPX. Experiments show that we have something misunderstood effect of epistasis on performance degradation of real-coded GAs. (shrink)

This paper presents the Real-coded Genetic Algorithms(RCGA) which can treat with high-dimensional ill-scaled structures, what is called, k -tablet structure. The k -tablet structure is the landscape that the scale of the fitness function is different between the k -dimensional subspace and the orthogonal (n-k) -dimensional subspace. The search speed of traditional RCGAs degrades when high-dimensional k -tablet structures are included in the landscape of fitness function. In this structure, offspring generated by crossovers is likely (...) to spread wider region than the region where the parental population covers. This phenomenon causes the stagnation of the search. To resolve this problem, we propose a new crossover LUNDX- m, which uses only m -dimensional latent variables. The effectiveness of the proposal method is tested with several benchmark functions including k -tablet structures and we show that our proposal method performs better than traditional crossovers especially when the dimensionality n is higher than 100. (shrink)

Marketing in the social network environment integrates current advanced internet and information technologies. This marketing method not only broadens marketing channels and builds a network communication platform but also meets the purchase needs of customers in the entire market and shortens customer purchases. The process is also an inevitable product of the development of the times. However, when companies use social networks for product marketing, they usually face the impact of multiple realistic factors. This article takes the maximization of influence (...) as the main idea to find seed users for product information dissemination and also considers the users’ interest preferences. The target users can influence the product, and the company should control marketing costs to obtain a larger marginal benefit. Based on this, this paper considers factors such as the scale of information diffusion, user interest preferences, and corporate budgets, takes the influence maximization model as a multiobjective optimization problem, and proposes a multiobjective maximization of influence model. To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. We use real social network data to verify the performance of models and methods. Experiments show that the proposed model can generate appropriate seed sets and can meet different purposes of information dissemination. Sensitivity analysis proves that our model is robust under different actual conditions. (shrink)

The Vehicle Routing Problem (VRP) has been extensively studied by different researchers from all over the world in recent years. Multiple solutions have been proposed for different variations of the problem, such as Capacitive Vehicle Routing Problem (CVRP), Vehicle Routing Problem with Time Windows (VRP-TW), Vehicle Routing Problem with Pickup and Delivery (VRPPD), among others, all of them with deterministic times. In the last years, researchers have been interested in including in their different models the variations that travel times may (...) experience when exposed to all kind of phenomena, mainly vehicle traffic. This article addresses the VRP from this perspective, proposing the design and implementation of a genetic algorithm based on neutrosophic theory for calculating the fitness function of each route, considering the variability and uncertainty present in travel times. A deterministic genetic algorithm is also implemented with the average travel times to compare it with the neutrosophic algorithm using simulation. As conclusion, a deterministic algorithm does not necessarily generate the best solution in the real world, full of uncertainty. Also, the quantification of uncertainty using neutrosophic theory can be used in route planning, opening a broad and interesting field of research for future investigations. (shrink)

The coaxiality and unbalance are the two important indexes to evaluate the assembly quality of an aeroengine. It often needs to be tested and disassembled repeatedly to meet the double-objective requirements at the same time. Therefore, an intelligent assembly method is urgently needed to directly predict the optimal assembly orientations of the rotors at each stage to meet the double-objective requirements simultaneously. In this study, an assembly optimization method for the multistage rotor of an aeroengine is proposed based on the (...) genetic algorithm. Firstly, a spatial location propagation model is developed to accurately predict the spatial position of each rotor after assembly. The alignment process of the assembly screw holes of the adjacent rotors is considered for the first time. Secondly, a new assembly optimization strategy is proposed to select different assembly data for the specific values of the coaxiality and unbalance, respectively. Finally, a double-objective fitness function is constructed based on the coaxiality and unbalance. The simulation and experimental results show that the assembly optimization method proposed in this study can be utilized to achieve synchronous optimization of the coaxiality and unbalance of an aeroengine during preassembly. (shrink)

This study aims to develop a hybrid model to represent the human mind from a functionalist point of view that can be adapted to artificial intelligence. The model is not a realistic theory of the neural network of the brain but an instrumentalist AI model, which means that there can be some other representative models too. It had been thought that the provability of an axiomatic system requires the completeness of a formal system. However, Gödel proved that no consistent formal (...) system can prove its own consistency. There is a paradoxical limit to provability. Both consistency and completeness are impossible together. These formal limits form the basis of our model: the hybrid model of the mind consists of a formal axiomatic system and an evolutionary algorithm (EA) of intelligence, which also includes the genetic algorithm (GA) of consciousness. The GA of consciousness operates based on intentions, functioning in conjunction with the principles of EA. This collaboration allows it to transcend the paradoxical formal limits of intelligence. However, GA overcomes the problem of paradoxicality at the cost of producing illusions. Following this collaboration gives us a GA operating system. After all, if the rational optimization task of the GA in question produces illusions, then the rationality must lie elsewhere in the paradox. (shrink)

For the application of the standard genetic algorithm in illustration art design, there are still problems such as low search efficiency and high complexity. This paper proposes an illustration art design model based on operator and clustering optimization genetic algorithm. First, during the operation of the genetic algorithm, the values of the crossover probability and the mutation probability are dynamically adjusted according to the characteristics of the population to improve the search efficiency of the algorithm, then the (...) k-medoids algorithm is introduced to optimize the clustering of the genetic algorithm, and a cost function is used to carry out and evaluate the quality of clustering to optimize the complexity of the original algorithm. In addition, a multiobjective optimization genetic algorithm with complex constraints based on group classification is proposed. This algorithm focuses on the problem of group diversity and uses k-means cluster analysis operation to solve the problem of group diversity. The algorithm divides the entire group into four subgroups and assigns appropriate fitness values to reflect the optimal preservation strategy. A large number of computer simulation calculations show that the algorithm can obtain a widely distributed and uniform Pareto optimal solution, the evolution speed is fast, usually only a few iterations can achieve a good optimization effect, and finally the improved genetic algorithm is used to design the random illustration art. The example simulation shows that the improved algorithm proposed in this paper can achieve higher artistic and innovative illustration art design. (shrink)

Based on the problem of e-commerce logistics and distribution network optimization, this paper summarizes the solution ideas and solutions proposed by domestic and foreign scholars and designs a method to optimize the B2C e-commerce logistics and distribution network by taking into account the special traffic conditions in the city. The logistics network optimization model is established and solved by combining various methods. Taking into account the new target requirements constantly proposed in the modern logistics environment, the vehicle path problem under (...) the generalized objective function is studied, and the multidimensional impact maximization problem in this type of problem is proposed and modeled. The problem follows from the path planning for emergency material delivery. Given locations, roads, and multiple classes of supplies in a map, each road allows vehicles to deliver each class of supplies with a certain probability. The goal of the problem is how to select a finite number of locations in the map as centers of supplies so that the number of locations that can be effectively covered by vehicle paths from them is maximized with the desired probability. For the first time, we used a hybrid genetic algorithm to optimize the e-commerce logistics path, and the optimized results are more reasonable than other algorithms. (shrink)

In this paper, to solve the problem of slow design of arts and crafts and to improve design efficiency and aesthetics, the existing big data and 3D technology are used to conduct an in-depth analysis of the optimization of the rapid design system of arts and crafts machine salt baking. In the system requirement analysis, the functional modules of this system are identified as nine functional modules such as design terminology management system and external information import function according to (...) the actual usage requirements. In the system design, the overall structure design, database design, and functional module design of the system are comprehensively elaborated, and the key issues such as 3D display and home layout generation algorithm based on reinforcement learning are analyzed and designed. In the implementation part of the system, the overall construction of the system and the composition of functional modules are introduced in detail and the main functional modules of the system are presented with interface diagrams. In the system implementation part, the overall system construction and functional module composition are introduced in detail, the main functional modules of the system are shown with interface diagrams, codes, and algorithms, and the specific implementation process of 3D display and soft layout algorithms are also explained in detail. The process of Surface Mount Technology big data processing and analysis is designed, and the design of SMT production line data collection scheme and real-time data processing architecture is completed. Based on the characteristics of SMT production line data, the K-means algorithm is used to detect data outliers and verify the accuracy of the method; also, the Spark-based association rule printing parameter recommendation model is designed, and the efficiency of the Apriori algorithm is significantly improved by parallelization. (shrink)

Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational methods (...) for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks. To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms. (shrink)

Children and adolescents are not indifferent to the dramatic impact of the COVID-19 pandemic, and the need to be forced to live in confinement. The change in life to which they have been abruptly subjected forces us to understand the state of their mental health in order to adequately address both their present and future needs. The present study was carried out with the intention of studying the consequences of confinement on anxiety, sleep routines and executive functioning of 1,028 children (...) and adolescents, aged from 6 to 18 years, residing in Spain to; assess if there are differences regarding these consequences in terms of sex and age; how anxiety affects executive functioning in males and females; and to examine the possible correlations between the measured variables. For this purpose, an online questionnaire containing five sections was designed: the first section gathers information on sociodemographic and health data, while the following sections gather information from different standardized scales which measure anxiety, sleep and executive functions, whose items were adapted in order to be completed by parents, and/or legal guardians. The statistical analyzes carried out highlights significant differences in executive functioning between males and females. In turn, in regards to age, greater difficulties were detected in anxiety in the 9 to 12 age group and greater sleep disturbances between 13 and 18 year olds. On the other hand, significant differences were found in intra-sexual executive functioning depending on whether they presented greater or lesser anxiety, with executive functioning being more tendentiously maladjusted in males than in females, revealing a significantly relevant effect size. Positive correlations are obtained between state anxiety and sleep and executive functioning alterations. Finally, through Path Analysis, it is verified that state anxiety is the variable with the greatest weight within the model that would explain the alteration in the executive functioning of the present sample. (shrink)

Findings suggest a positive impact of bilingualism on cognition, including the later onset of dementia. However, it is not clear to what extent these effects are influenced by variations in attentional control demands in response to specific task requirements. In this study, 20 bilingual and 20 monolingual older adults performed a task-switching task under explicit task-cuing vs. memory-based switching conditions. In the cued condition, task switches occurred in random order and a visual cue signaled the next task to be performed. (...) In the memory-based condition, the task alternated after every second trial in a predictable sequence without presenting a cue. The performance of bilinguals did not vary across experimental conditions, whereas monolinguals experienced a pronounced increase in response latencies and error rates in the cued condition. Both groups produced similar switch costs and mixing costs, but bilinguals produced them with lower response latencies. The cognitive benefits of bilingualism seem not to apply to executive functions per se but to affect specific cognitive processes that involve task-relevant context processing. The present results suggest that lifelong bilingualism could promote in older adults a flexible adjustment to environmental cues, but only with increased task demands. However, due to the small sample size, the results should be interpreted with caution. (shrink)

In this paper, we simulate the estimation of motion through an interframe difference detection function model and investigate the spatial-temporal context information correlation filtering target tracking algorithm, which is complex and computationally intensive. The basic theory of spatiotemporal context information and correlation filtering is studied to construct a fast target tracking method. The different computational schemes are designed for the flow of multiframe target detection from background removal to noise reduction, to single-frame detection, and finally to multiframe detection, respectively. (...) This enables the ground-based telescope to effectively detect spatial targets in dense stellar backgrounds in both modes. The method is validated by simulations and experiments and can meet the requirements of real projects. The interframe bit attitude estimation is optimized by using the beam-parity method to reduce the interframe estimation noise; a global optimization strategy based on the bit attitude map is used in the back end to reduce the system computation amount and make the global bit attitude estimation more accurate; a loop detection based on the word pocket model is added to the system to reduce the cumulative error. (shrink)

A large number of modern communication technologies and sensing technologies are incorporated into the smart grid, which makes its structure unique. The centralized optimized dispatch method of traditional power grids is difficult to achieve effective dispatch of smart grids. Based on the analysis of power generation plan and maintenance plan optimization model, this paper establishes a smart grid power generation and maintenance collaborative optimization model with distributed renewable energy. The objective function of this collaborative optimization problem is the operating (...) cost of conventional units, the cost of wind power generation, and the cost of overhauling units; the constraints considered mainly include system constraints and overhaul constraints. The solution method of combinatorial optimization is analyzed, and the genetic optimization algorithm adopted in this paper is selected and discussed. According to the characteristics of the system, various loads are modeled, and power supply constraints are considered. By establishing an effective objective function, the adjustable load scheduling problem is transformed into a solvable optimal control problem. Taking into account the uncertain factors in the system, the advantage of the real-time control system is that it can realize the dynamic update scheduling of the load, so it is more in line with the requirements of the actual system. The real-time algorithm proposed in the paper is based on a distributed control strategy, which can not only realize dynamic compensation for random fluctuations in renewable energy power generation but also satisfy the load curve optimization under the premise of making full use of power supply resources. In addition, simulation experiments compare the load dispatching capabilities of the proposed algorithm with the existing algorithms, thereby verifying the performance of the proposed method. (shrink)

The origin of the genetic code has been attributed in part to an accidental assignment of codons to amino acids. Although several lines of evidence indicate the subsequent expansion and improvement of the genetic code, the hypothesis of Francis Crick concerning a frozen accident occurring at the early stage of genetic code evolution is still widely accepted. Considering Crick’s hypothesis, mathematical descriptions of hypothetical scenarios involving a huge number of possible coexisting random genetic codes could be (...) very important to explain the origin and evolution of a selected genetic code. This work aims to contribute in this regard, that is, it provides a theoretical framework in which statistical parameters of error functions are calculated. Given a genetic code and an amino acid property, the functional code robustness is estimated by means of a known error function. In this work, using analytical calculations, general expressions for the average and standard deviation of the error function distributions of completely random codes with standard stop codons were obtained. As a possible biological application of these results, any set of amino acids and any pure or mixed amino acid properties can be used in the calculations, such that, in case of having to select a set of amino acids to create a genetic code, possible advantages of natural selection of the genetic codes could be discussed. (shrink)

Many real-world problems entail multiple conflicting objectives, which makes multiobjective optimization an important subject. Much attention has been paid to Genetic Algorithm as a potent multiobjective optimization method, and the effectiveness of its hybridization with local search has recently been reported in the literature. However, there have been a relatively small number of studies on LS methods for multiobjective function optimization. Although each of the existing LS methods has some strong points, they have respective drawbacks such as (...) high computational cost and inefficiency of improving objective functions. Hence, a more effective and efficient LS method is being sought, which can be used to enhance the performance of the hybridization. Pareto descent directions are defined in this paper as descent directions to which no other descent directions are superior in improving all objective functions. Moving solutions in such directions is expected to maximally improve all objective functions simultaneously. This paper proposes a new LS method, Pareto Descent Method, which finds Pareto descent directions and moves solutions in such directions. In the case part or all of them are infeasible, it finds feasible Pareto descent directions or descent directions as necessary and moves solutions in these directions. PDM finds these directions by solving linear programming problems. Thus, it is computationally inexpensive. Experiments have shown that PDM is superior to existing methods. (shrink)

Attention-deficit hyperactivity disorder is one of the most common neurodevelopmental disorders in children and adolescents. People who have this disorder are characterized by presenting difficulties in the processes of sustained attention, being very active, and having poor control of their impulses. Despite the high prevalence of this disorder and the existence of various tests used for its diagnosis, few data are available regarding the usefulness and diagnostic validity of these tools. Given the difficulties that these subjects present in executive (...) class='Hi'>functions, the aim of this study was to evaluate whether the Neuropsychological Assessment of Executive Functions battery for Children allows to establish specific profiles of executive performance for people with attention-deficit hyperactivity disorder. The sample was made up of 197 participants of both sexes, aged between 6 and 12 years age. A nonexperimental design was followed, using a comparative descriptive study method. The results indicated that the scales of phonological fluency, color path, rings, and interference are the most associated with the diagnosis of ADHD, providing data on inhibition, mental flexibility, sustained and selective attention, planning, verbal fluency, and working memory, among others. The practical implication of these results is in line with providing support in the clinical diagnosis that is carried out in children’s mental health units. In addition, the ENFEN tool can be valued as a suitable psychometric instrument in the psychoeducational field, helping professionals in a school environment to be more aware of the areas of cognitive development in which a student diagnosed with ADHD will have more difficulties and, in doing so, providing more adjusted and effective psychopedagogical measures when it comes to supporting students in their adaptation to the school environment. (shrink)

For enhancing the stability of the microgrid operation, this paper proposes an optimization model considering the small-signal stability constraint. Due to the nonsmooth property of the spectral abscissa function, the droop controller parameters’ optimization is a nonsmooth optimization problem. The Sequential Quadratic Programming with Gradient Sampling is implemented to optimize the droop controller parameters for solving the nonsmooth problem. The SQP-GS method can guarantee the solution of the optimization problem globally and efficiently converges to stationary points with probability (...) of one. In the current iteration, the gradient of the nonsmooth function can be evaluated on a set of randomly generated nearby points by computing closed-form sensitivities. A test on the microgrid system shows that the optimality and the efficiency of the SQP-GS are better than those of the heuristic algorithms. (shrink)

Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms belong to nature-inspired algorithms and swarm intelligence paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE (...) Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity. (shrink)

Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization, firefly algorithm, ant colony optimization, and bat algorithm have gained much popularity and they have successfully tackled various test suites of (...) benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation. The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems. (shrink)

Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is (...) introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments. (shrink)

Evolution is often characterized as a tinkerer that creates efficient but messy solutions to problems. We analyze the nature of the problems that arise when we try to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem – solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show that the reason for (...) why evolutionary designs are often hard to understand is that they exhibit non-modular functionality, and that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation. (shrink)

The optimization of high-dimensional functions is an important problem in both science and engineering. Wolf pack algorithm is a technique often used for computing the global optimum of a multivariable function. In this paper, we develop a new wolf pack algorithm that can accurately compute the optimal value of a high-dimensional function. First, chaotic opposite initialization is designed to improve the quality of initial solution. Second, the disturbance factor is added in the scouting process to enhance the (...) searching ability of wolves, and an adaptive step length is designed to enhance the global searching ability to prevent wolves from falling into the local optimum effectively. A set of standard test functions are selected to test the performance of the proposed algorithm, and the test results are compared with other algorithms. The high-dimensional and ultrahigh-dimensional functions are tested. The experimental results show that the proposed algorithm features in good global convergence, high accuracy calculation, strong robustness, and excellent performance in high-dimensional functions. (shrink)

An improved variant of the Jaya optimization algorithm, called Jaya2, is proposed to enhance the performance of the original Jaya sacrificing its algorithmic design. The proposed approach arranges the solutions in a ring topology to reduce the likelihood of premature convergence. In addition, the population size reduction is used to automatically adjust the population size during the optimization process. Moreover, the translation dependency problem of the original Jaya is discussed, and an alternative solution update operation is proposed. To test Jaya2, (...) we compare it with nine different optimization methods on the CEC 2020 benchmark functions and the CEC 2011 real-world optimization problems. The results show that Jaya2 is highly competitive on the tested problems where it generally outperforms most approaches. Having an easy-to-implement approach with little parameter tuning is highly desirable since researchers from different disciplines with basic programming skills can use it to solve their optimization problems. (shrink)

Function optimization underlies many real-world problems and hence is an important research subject. Most of the existing optimization methods were developed to solve primarily unconstrained problems. Since real-world problems are often constrained, appropriate handling of constraints is necessary in order to use the optimization methods. In particular, the performances of some methods such as Genetic Algorithms can be substantially undermined by ineffective constraint handling. Despite much effort devoted to the studies of constraint-handling methods, it has been (...) reported that each of them has certain limitations. Hence, further studies for designing more effective constraint-handling methods are needed. For this reason, we investigated the guidelines for a method to effectively handle constraints. The guidelines are that the method 1) takes the approach of repair operators, 2) monotonically decreases both the number of violated constraints and constraint violations, and 3) searches over the boundaries of violated constraints. Based on these guidelines, we designed a new constraint-handling method Pareto Descent Repair operator in which ideas derived from multi-objective local search and gradient projection method are incorporated. Experiments comparing GA that use PDR and some of the existing constraint-handling methods confirmed the effectiveness of PDR. (shrink)

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating (...) uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. (shrink)

Optimization, in general, is regarded as the process of finding optimal values for the variables of a given problem in order to minimize or maximize one or more objective function(s). Brain storm optimization (BSO) algorithm solves a complex optimization problem by mimicking the human idea generating process, in which a group of people solves a problem together. The aim of this paper is to present hybrid BSO algorithm solutions in the past 5 years. This study could be divided into (...) two parts: strategies and applications. In the first part, different strategies for the hybrid BSO algorithms intended to improve the various ability of the original BSO algorithm are displayed. In the second part, the real-world applications in the past five years in optimization, prediction and feature selection processes are presented. (shrink)

The irreversibility in time, the multicausality on lines, and the uncertainty of feedbacks make economic systems and the predictions of economic chaotic time series possess the characteristics of high dimensionalities, multiconstraints, and complex nonlinearities. Based on genetic algorithm and fuzzy rules, the chaotic genetics combined with fuzzy decision-making can use simple, fast, and flexible means to complete the goals of automation and intelligence that are difficult to traditional predicting algorithms. Moreover, the new combined method’s ergodicity can perform nonrepetitive searches (...) in a global scope, hence improving the algorithm’s accuracy and efficiency. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the prediction of economic chaotic time series, elaborated the development background, current status, and future challenges of the combined algorithm of chaotic genetics with fuzzy decision, introduced the basic principles of chaotic genetic algorithm and fuzzy decision algorithm, constructed a prediction model for economic chaotic time series, performed parameter synchronization optimization and moderate function construction, analyzed the prediction processes of economic chaotic time series, conducted phase space reconstruction and correlation dimension calculation, and finally carried out a simulation experiment with its result analysis. The study results show that the algorithm of chaotic genetics combined with fuzzy decision-making can dynamically adjust chaotic mutation operators and summarize fussy expert experiences. The phase space of its reconstructed chaotic attractor has high-precision predictability and can find orderly processes from changeable economic results, which in turn can be used to analyze and predict the complex economic chaotic time series. The study results of this paper provide a reference for further research on predictive analysis of economic chaotic time series based on chaotic genetics combined with fuzzy decision algorithm. (shrink)

Arithmetic optimization algorithm (AOA) is one of the recently proposed population-based metaheuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), division (D), subtraction (S), and addition (A). Being a new metaheuristic algorithm, the need for a performance evaluation of AOA is significant to the global optimization research community and specifically to nature-inspired metaheuristic enthusiasts. This article aims to evaluate the influence of the algorithm control parameters, namely, population size (...) and the number of iterations, on the performance of the newly proposed AOA. In addition, we also investigated and validated the influence of different initialization schemes available in the literature on the performance of the AOA. Experiments were conducted using different initialization scenarios and the first is where the population size is large and the number of iterations is low. The second scenario is when the number of iterations is high, and the population size is small. Finally, when the population size and the number of iterations are similar. The numerical results from the conducted experiments showed that AOA is sensitive to the population size and requires a large population size for optimal performance. Afterward, we initialized AOA with six initialization schemes, and their performances were tested on the classical functions and the functions defined in the CEC 2020 suite. The results were presented, and their implications were discussed. Our results showed that the performance of AOA could be influenced when the solution is initialized with schemes other than default random numbers. The Beta distribution outperformed the random number distribution in all cases for both the classical and CEC 2020 functions. The performance of uniform distribution, Rayleigh distribution, Latin hypercube sampling, and Sobol low discrepancy sequence are relatively competitive with the Random number. On the basis of our experiments’ results, we recommend that a solution size of 6,000, the number of iterations of 100, and initializing the solutions with Beta distribution will lead to AOA performing optimally for scenarios considered in our experiments. (shrink)

Schedule planning is one of the most crucial issues for any airline company, because the profit of the company directly depends on the efficiency of the schedule. This paper presents a novel scheduling method which solves problems related to time scheduling, fleet assignment and maintenance routing simultaneously by Genetic Algorithms. Every schedule constraint is embeded in the fitness function, which is described as an object oriented model and works as a simulater developing itself over time, and whose solution (...) is executable without human correction. The schedular is able to solve the problems involving several hundred flights in a few hours, and the solutions are superior or equivalent to those by human experts in terms of the estimated profit. (shrink)

This paper sheds light on the shift that is taking place from the practice of ‘coding’, namely developing programs as conventional in the software community, to the practice of ‘curing’, an activity that has emerged in the last few years in Deep Learning (DL) and that amounts to curing the data regime to which a DL model is exposed during training. Initially, the curing paradigm is illustrated by means of a study-case on autonomous vehicles. Subsequently, the shift from coding to (...) curing is analysed taking into consideration the epistemological notions, central in the philosophy of computer science, of function, implementation, and correctness. First, it is illustrated how, in the curing paradigm, the functions performed by the trained model depend much more on dataset curation rather than on the model algorithms which, in contrast with the coding paradigm, do not comply with requested specifications. Second, it is highlighted how DL models cannot be considered implementations according to any of the available definitions of implementation that follow an intentional theory of functions. Finally, it is argued that DL models cannot be evaluated in terms of their correctness but rather in their experimental computational validity. (shrink)

Professor Robinson provides a new critique of the often neglected problem of classification within the criminal law. He presents a discussion of the present conceptual framework of the law, and offers explanations of how and why formal structures do not match the operation of law in practice. In this scholarly exposition of applied criminal theory, Robinson argues that the current operational structure of the criminal law fails to take account of its different functions. He goes on to suggest new (...) sample codes of criminal conduct and criminal adjudication which mark a real departure from the pragmatic approach which presently dominates code-making. This rounded exploration of the structure of systems of criminal law is an important work for law teachers and policy makers world-wide. (shrink)

Professor Robinson provides a new critique of the often neglected problem of classification within the criminal law. He presents a discussion of the present conceptual framework of the law, and offers explanations of how and why formal structures do not match the operation of law in practice. In this scholarly exposition of applied criminal theory, Robinson argues that the current operational structure of the criminal law fails to take account of its different functions. He goes on to suggest new (...) sample codes of criminal conduct and criminal adjudication which mark a real departure from the pragmatic approach which presently dominates code-making. This rounded exploration of the structure of systems of criminal law is an important work for law teachers and policy makers world-wide. (shrink)

Code-based cryptography is currently the second most promising post-quantum mathematical tool for quantum-resistant algorithms. Since in 2022 the first post-quantum standard Key Encapsulation Mechanism, Kyber (a latticed-based algorithm), was selected to be established as standard, and after that the National Institute of Standards and Technology post-quantum standardization call focused in code-based cryptosystems. Three of the four candidates that remain in the fourth round are code-based algorithms. In fact, the only non-code-based algorithm (SIKE) is now considered vulnerable. Due to this landscape, (...) it is crucial to update previous results about these algorithms and their functioning. The Fujisaki-Okamoto transformation is a key part of the study of post-quantum algorithms and in this work we focus our analysis on Classic McEliece, BIKE and HQC proposals, and how they apply this transformation to obtain IND-CCA semantic security. Since after security the most important parameter in the evaluation of the algorithms is performance, we have compared the performance of the code-based algorithms of the NIST call considering the same architecture for all of them. (shrink)

To realize the coordinated and sustainable development of hydropower projects and regional society, comprehensively evaluating hydropower projects’ influence is critical. Usually, hydropower project development has an impact on environmental geology and social and regional cultural development. Based on comprehensive consideration of complicated geological conditions, fragile ecological environment, resettlement of reservoir area, and other factors of future hydropower development in each country, we have constructed a comprehensive evaluation index system of hydropower projects, including 4 first-level indicators of social economy, environment, safety, (...) and fairness, which contain 26 second-level indicators. To solve the problem that existing models cannot evaluate dynamic nonlinear optimization, a projection pursuit model is constructed by using rough set reduction theory to simplify the index. Then, an accelerated genetic algorithm based on real number coding is used to solve the model and empirical study is carried out with the Y hydropower station as a sample. The evaluation results show that the evaluation index system and assessment model constructed in our paper effectively reduce the subjectivity of index weight. Applying our model to the social impact assessment of related international hydropower projects can not only comprehensively analyze the social impact of hydropower projects but also identify important social influencing factors and effectively analyze the social impact level of each dimension. Furthermore, SIA assessment can be conducive to project decision-making, avoiding social risks and social stability. (shrink)

We formulate the problem of permuting a matrix to block angular form as the combinatorial minimization of an objective function. We motivate the use of simulated annealing (SA) as an optimization tool. We then introduce a heuristic temperature dependent penalty function in the simulated annealing cost function, to be used instead of the real objective function being minimized. Finally we show that this temperature dependent penalty function version of simulated annealing consistently outperforms the standard (...) simulated annealing approach, producing, with smaller running times, better solutions. We believe that the use of a temperature dependent penalty function may be useful in developing SA algorithms for other combinatorial problems. -/- . (shrink)

In the context of constructive reverse mathematics, we show that weak Kőnig's lemma () implies that every pointwise continuous function is induced by a code in the sense of reverse mathematics. This, combined with the fact that implies the Fan theorem, shows that implies the uniform continuity theorem: every pointwise continuous function has a modulus of uniform continuity. Our results are obtained in Heyting arithmetic in all finite types with quantifier‐free axiom of choice.

Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental optimization algorithm (...) to realize emergency scheduling of public resources, neglecting the improvement process of the objective function, which leads to unsatisfactory scheduling effect. An emergency scheduling method of cloud computing platform network public resources based on genetic algorithm is proposed. With emergency public resource scheduling time cost and transportation cost minimizing target, initial population by Hamming distance constraints, emergency scheduling model, and the corresponding objective function improvement as the fitness function, the genetic algorithm to individual selection and crossover and mutation probability were optimized and complete the public emergency resources scheduling. Experimental results show that the proposed method can effectively improve the efficiency of emergency resource scheduling, and the reliability of emergency scheduling is better. (shrink)

An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals—user-generated intent-to-move neural activity recorded from cerebral cortical motor areas—to drive electrical stimulation of individual muscles or muscle synergies. BCI-FES (...) interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity. (shrink)