Results for ' Search Optimization'

982 found
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  1.  25
    Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization.Xiashuang Wang, Guanghong Gong, Ni Li & Shi Qiu - 2019 - Frontiers in Human Neuroscience 13:424082.
    In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an (...)
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  2. Optimisation and mathematical explanation: doing the Lévy Walk.Sam Baron - 2014 - Synthese 191 (3).
    The indispensability argument seeks to establish the existence of mathematical objects. The success of the indispensability argument turns on finding cases of genuine extra- mathematical explanation. In this paper, I identify a new case of extra- mathematical explanation, involving the search patterns of fully-aquatic marine predators. I go on to use this case to predict the prevalence of extra- mathematical explanation in science.
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  3.  3
    Adaptive large-neighbourhood search for optimisation in answer-set programming.Thomas Eiter, Tobias Geibinger, Nelson Higuera Ruiz, Nysret Musliu, Johannes Oetsch, Dave Pfliegler & Daria Stepanova - 2024 - Artificial Intelligence 337 (C):104230.
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  4.  37
    Evolutionary Search with Multiple Utopian Reference Points in Decomposition-Based Multiobjective Optimization.Wu Lin, Qiuzhen Lin, Zexuan Zhu, Jianqiang Li, Jianyong Chen & Zhong Ming - 2019 - Complexity 2019:1-22.
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  5.  17
    A variable neighbourhood search for minimization of operation times through warehouse layout optimization.Jon Díaz, Haizea Rodriguez, Jenny Fajardo-Calderín, Ignacio Angulo & Enrique Onieva - 2024 - Logic Journal of the IGPL 32 (4):688-699.
    For companies involved in the supply chain, proper warehousing management is crucial. Warehouse layout arrangement and operation play a critical role in a company’s ability to maintain and improve its competitiveness. Reducing costs and increasing efficiency are two of the most crucial warehousing goals. Deciding on the best warehouse layout is a remarkable optimization problem. This paper uses an optimization method to set bin allocations within an automated warehouse with particular characteristics. The warehouse’s initial layout and the automated (...)
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  6.  16
    Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks.Weixiong Zhang, Guandong Wang, Zhao Xing & Lars Wittenburg - 2005 - Artificial Intelligence 161 (1-2):55-87.
  7.  21
    Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm.T. V. SureshKumar & P. Lakshminarayana - 2020 - Journal of Intelligent Systems 30 (1):59-72.
    Software testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that (...)
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  8.  22
    Improved local search for the minimum weight dominating set problem in massive graphs by using a deep optimization mechanism.Jiejiang Chen, Shaowei Cai, Yiyuan Wang, Wenhao Xu, Jia Ji & Minghao Yin - 2023 - Artificial Intelligence 314 (C):103819.
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  9.  17
    Explorative anytime local search for distributed constraint optimization.Roie Zivan, Steven Okamoto & Hilla Peled - 2014 - Artificial Intelligence 212 (C):1-26.
  10.  86
    Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?Douglas B. Kell - 2012 - Bioessays 34 (3):236-244.
    A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best (...)
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  11.  15
    Memory intensive AND/OR search for combinatorial optimization in graphical models.Radu Marinescu & Rina Dechter - 2009 - Artificial Intelligence 173 (16-17):1492-1524.
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  12.  15
    An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm.Nacer Farajzadeh & Asgarali Bouyer - 2019 - Journal of Intelligent Systems 29 (1):1-18.
    Among the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization (...)
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  13.  51
    Improved Optimization for Wastewater Treatment and Reuse System Using Computational Intelligence.Zong Woo Geem, Sung Yong Chung & Jin-Hong Kim - 2018 - Complexity 2018:1-8.
    River water pollution by wastewater can cause significant negative impact on the aquatic sustainability. Hence, accurate modeling of this complicated system and its cost-effective treatment and reuse decision is very important because this optimization process is related to economic expenditure, societal health, and environmental deterioration. In order to optimize this complex system, we may consider three treatment or reuse options such as microscreening filtration, nitrification, and fertilization-oriented irrigation on top of two existing options such as settling and biological oxidation. (...)
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  14.  18
    AND/OR Branch-and-Bound search for combinatorial optimization in graphical models.Radu Marinescu & Rina Dechter - 2009 - Artificial Intelligence 173 (16-17):1457-1491.
  15. Global Optimization Studies on the 1-D Phase Problem.Jim Marsh, Martin Zwick & Byrne Lovell - 1996 - Int. J. Of General Systems 25 (1):47-59.
    The Genetic Algorithm (GA) and Simulated Annealing (SA), two techniques for global optimization, were applied to a reduced (simplified) form of the phase problem (RPP) in computational crystallography. Results were compared with those of "enhanced pair flipping" (EPF), a more elaborate problem-specific algorithm incorporating local and global searches. Not surprisingly, EPF did better than the GA or SA approaches, but the existence of GA and SA techniques more advanced than those used in this study suggest that these techniques still (...)
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  16.  27
    An Improved Multiobjective Quantum-Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism.Fei Han, Yu-Wen-Tian Sun & Qing-Hua Ling - 2018 - Complexity 2018:1-22.
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  17.  21
    A modified biogeography-based optimization algorithm with improved mutation operator for job shop scheduling problem with time lags.Madiha Harrabi, Olfa Belkahla Driss & Khaled Ghedira - forthcoming - Logic Journal of the IGPL.
    This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra delays can be introduced between successive operations of the same job. It belongs to a category of problems known as NP-hard problem due to large solution space. Biogeography-based optimization is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon in 2008 (...)
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  18.  12
    Cut-and-solve: An iterative search strategy for combinatorial optimization problems.Sharlee Climer & Weixiong Zhang - 2006 - Artificial Intelligence 170 (8-9):714-738.
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  19.  15
    Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme.Yu Cui, Shunfu Jin, Wuyi Yue & Yutaka Takahashi - 2021 - Complexity 2021:1-18.
    As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. As a (...)
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  20.  85
    Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification.Xiaojia Ye, Wei Liu, Hong Li, Mingjing Wang, Chen Chi, Guoxi Liang, Huiling Chen & Hailong Huang - 2021 - Complexity 2021:1-23.
    The whale optimization algorithm is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of (...)
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  21.  13
    Optimization of Quantitative Financial Data Analysis System Based on Deep Learning.Meiyi Liang - 2021 - Complexity 2021:1-11.
    In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and displays the retrieval (...)
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  22.  25
    Black widow optimization for reducing the target uncertainties in localization wireless sensor networks.Rubén Ferrero-Guillén, José-Manuel Alija-Pérez, Alberto Martínez-Gutiérrez, Rubén Álvarez, Paula Verde & Javier Díez-González - 2024 - Logic Journal of the IGPL 32 (6):971-985.
    Localization Wireless Sensor Networks (WSN) represent a research topic with increasing interest due to their numerous applications. However, the viability of these systems is compromised by the attained localization uncertainties once implemented, since the network performance is highly dependent on the sensors location. The Node Location Problem (NLP) aims to obtain the optimal distribution of sensors for a particular environment, a problem already categorized as NP-Hard. Furthermore, localization WSN usually perform a sensor selection for determining which nodes are to be (...)
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  23.  56
    Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm.Lu Jing - 2020 - Complexity 2020:1-10.
    This paper is aimed at the problems of low accuracy, long recognition time, and low recognition efficiency in English speech recognition. In order to improve the accuracy and efficiency of English speech recognition, an improved ant colony algorithm is used to deal with the dynamic time planning problem. The core is to adopt an adaptive volatilization coefficient and dynamic pheromone update strategy for the basic ant colony algorithm. Using new state transition rules and optimal ant parameter selection and other improved (...)
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  24.  9
    An Improved Particle Swarm Optimization-Powered Adaptive Classification and Migration Visualization for Music Style.Xiahan Liu - 2021 - Complexity 2021:1-10.
    Based on the adaptive particle swarm algorithm and error backpropagation neural network, this paper proposes methods for different styles of music classification and migration visualization. This method has the advantages of simple structure, mature algorithm, and accurate optimization. It can find better network weights and thresholds so that particles can jump out of the local optimal solutions previously searched and search in a larger space. The global search uses the gradient method to accelerate the optimization and (...)
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  25.  46
    Cat swarm optimization algorithm based on the information interaction of subgroup and the top-N learning strategy.Wang Miao, Yu Haipeng & Li Songyang - 2022 - Journal of Intelligent Systems 31 (1):489-500.
    Because of the lack of interaction between seeking mode cats and tracking mode cats in cat swarm optimization, its convergence speed and convergence accuracy are affected. An information interaction strategy is designed between seeking mode cats and tracking mode cats to improve the convergence speed of the CSO. To increase the diversity of each cat, a top-N learning strategy is proposed during the tracking process of tracking mode cats to improve the convergence accuracy of the CSO. On ten standard (...)
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  26.  21
    Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations.Deepak Mishra & Venkatesh Ss - 2020 - Journal of Intelligent Systems 30 (1):142-164.
    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 (...)
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  27.  31
    A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning.Yongquan Zhou & Zhonghua Tang - 2015 - Journal of Intelligent Systems 24 (1):69-83.
    Uninhabited combat air vehicle path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization algorithm based on the particle swarm optimization algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local (...)
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  28.  53
    Educational Information System Optimization for Artificial Intelligence Teaching Strategies.Taotang Liu, Zhongxin Gao & Honghai Guan - 2021 - Complexity 2021:1-13.
    Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a (...)
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  29.  9
    Algorithms for optimization.Mykel J. Kochenderfer - 2019 - Cambridge, Massachusetts: The MIT Press. Edited by Tim A. Wheeler.
    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 (...)
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  30.  15
    A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems.Wali Khan Mashwani, Ruqayya Haider & Samir Brahim Belhaouari - 2021 - Complexity 2021:1-18.
    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 (...)
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  31.  21
    最適解の位置にロバストな実数値 GA を実現する Toroidal Search Space Conversion の提案.Yamamura Masayuki Someya Hiroshi - 2001 - Transactions of the Japanese Society for Artificial Intelligence 16 (3):333-343.
    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 (...) 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)
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  32. Manipulating search engine algorithms: the case of Google.Judit Bar-Ilan - 2007 - Journal of Information, Communication and Ethics in Society 5 (2/3):155-166.
    PurposeTo investigate how search engine users manipulate the rankings of search results. Search engines employ different ranking methods in order to display the “best” results first. One of the ranking methods is PageRank, where the number of links pointing to the page influences its rank. The “anchor text,” the clickable text of the hypertext link is another “ingredient” in the ranking method. There are a number of cases where the public challenged the Google's ranking, by creating a (...)
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  33.  26
    Stochastic Travelling Advisor Problem Simulation with a Case Study: A Novel Binary Gaining-Sharing Knowledge-Based Optimization Algorithm.Said Ali Hassan, Yousra Mohamed Ayman, Khalid Alnowibet, Prachi Agrawal & Ali Wagdy Mohamed - 2020 - Complexity 2020:1-15.
    This article proposes a new problem which is called the Stochastic Travelling Advisor Problem in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times (...)
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  34.  16
    A Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation.Hongli Yu, Yuelin Gao & Jincheng Wang - 2020 - Complexity 2020:1-23.
    In order to solve the shortcomings of particle swarm optimization in solving multiobjective optimization problems, an improved multiobjective particle swarm optimization algorithm is proposed. In this study, the competitive strategy was introduced into the construction process of Pareto external archives to speed up the search process of nondominated solutions, thereby increasing the speed of the establishment of Pareto external archives. In addition, the descending order of crowding distance method is used to limit the size of external (...)
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  35.  18
    Surrogate-based optimization of learning strategies for additively regularized topic models.Maria Khodorchenko, Nikolay Butakov, Timur Sokhin & Sergey Teryoshkin - 2023 - Logic Journal of the IGPL 31 (2):287-299.
    Topic modelling is a popular unsupervised method for text processing that provides interpretable document representation. One of the most high-level approaches is additively regularized topic models (ARTM). This method features better quality than other methods due to its flexibility and advanced regularization abilities. However, it is challenging to find an optimal learning strategy to create high-quality topics because a user needs to select the regularizers with their values and determine the order of application. Moreover, it may require many real runs (...)
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  36.  20
    Best Polynomial Harmony Search with Best β-Hill Climbing Algorithm.Eugene Santos & Iyad Abu Doush - 2020 - Journal of Intelligent Systems 30 (1):1-17.
    Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel and tournament selection in memory (...)
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  37.  18
    Generalization and Search in Risky Environments.Eric Schulz, Charley M. Wu, Quentin J. M. Huys, Andreas Krause & Maarten Speekenbrink - 2018 - Cognitive Science 42 (8):2592-2620.
    How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi‐armed bandit task, we show that participants’ behavior is aligned (...)
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  38.  17
    A Modified Salp Swarm Algorithm Based on the Perturbation Weight for Global Optimization Problems.Yuqi Fan, Junpeng Shao, Guitao Sun & Xuan Shao - 2020 - Complexity 2020:1-17.
    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 (...)
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  39.  88
    Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models.Yingjie Song, Daqing Wu, Ali Wagdy Mohamed, Xiangbing Zhou, Bin Zhang & Wu Deng - 2021 - Complexity 2021:1-22.
    In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy (...)
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  40.  13
    Modeling and Parameter Optimization of Dynamic Characteristic Variables of Ballast Bed during Operation for Dynamic Track Stabilizer.Bo Yan & Jingjing Yang - 2021 - Complexity 2021:1-11.
    The high traffic density of railway line causes ballasted track to be extremely busy, and thus it is particularly important to improve the efficiency during railway maintenance. The changing law of dynamic characteristics of ballast bed during operation for the dynamic track stabilizer is conducive to optimize simulation analysis of the vehicle-track system, so as to provide an optimized choice of operating parameters for promoting the pertinence and efficiency of dynamic track stabilizer. This paper presents the acceleration response of vehicle-track-subgrade (...)
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  41.  28
    Efficient Conical Area Differential Evolution with Biased Decomposition and Dual Populations for Constrained Optimization.Weiqin Ying, Bin Wu, Yu Wu, Yali Deng, Hainan Huang & Zhenyu Wang - 2019 - Complexity 2019:1-18.
    The constraint-handling methods using multiobjective techniques in evolutionary algorithms have drawn increasing attention from researchers. This paper proposes an efficient conical area differential evolution algorithm, which employs biased decomposition and dual populations for constrained optimization by borrowing the idea of cone decomposition for multiobjective optimization. In this approach, a conical subpopulation and a feasible subpopulation are designed to search for the global feasible optimum, along the Pareto front and the feasible segment, respectively, in a cooperative way. In (...)
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  42.  22
    MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem.Yusuf Benson Baha, Gregory Wajiga, Aderemi Adewumi Oluyinka & Nachamada Vachaku Blamah - 2020 - Journal of Intelligent Systems 30 (1):413-428.
    This paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with one another in a coordinated search effort within their environment and the solution space. A discrete version of the original multi-agent model is presented and applied to the Travelling Salesman Problem. Based on the (...)
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  43.  14
    Knowledge and Behavior-Driven Fruit Fly Optimization Algorithm for Field Service Scheduling Problem with Customer Satisfaction.Bin Wu, Hui-Jun Jiang, Chao Wang & Min Dong - 2021 - Complexity 2021:1-14.
    The field service scheduling problem is the key problem in field services. Field service pays particular attention to customer experience, that is, customer satisfaction. Customer satisfaction described by customer behavior characteristics based on the prospect theory is considered as the primary optimization goal in this paper. The knowledge of the insertion feasibility on the solution is analysed based on the skill constraint and time window. According to the knowledge, an initialization method based on the nearest heuristic algorithm is constructed. (...)
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  44.  13
    Smart City Landscape Design Based on Improved Particle Swarm Optimization Algorithm.Wenting Yao & Yongjun Ding - 2020 - Complexity 2020:1-10.
    Aiming at the shortcomings of standard particle swarm optimization algorithms that easily fall into local optimum, this paper proposes an optimization algorithm that improves quantum behavioral particle swarms. Aiming at the problem of premature convergence of the particle swarm algorithm, the evolution speed of individual particles and the population dispersion are used to dynamically adjust the inertia weights to make them adaptive and controllable, thereby avoiding premature convergence. At the same time, the natural selection method is introduced into (...)
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  45.  18
    Research on Wireless Sensor Network Coverage Path Optimization Based on Biogeography-Based Optimization Algorithm.Guojun Chen, Xiangdong Qin, Ningsheng Fang & Wenbo Xu - 2021 - Complexity 2021:1-8.
    Path selection is one of the key technologies of wireless sensor network. A reasonable choice of coverage path can improve the service quality of WSN and extend the life cycle of WSN. Biogeography-based optimization is widely used in the field of cluster intelligent optimization because its search method has a better incentive mechanism for population evolution. In this paper, the move-in and move-out operation and mutation operation of the BBO algorithm enable WSN to find an efficient routing (...)
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  46.  15
    A Pseudo-Deterministic Noisy Extremal Optimization algorithm for the pairwise connectivity Critical Node Detection Problem.Noémi Gaskó, Mihai-Alexandru Suciu, Rodica Ioana Lung & Tamás Képes - forthcoming - Logic Journal of the IGPL.
    The critical node detection problem is a central task in computational graph theory due to its large applicability, consisting in deleting $k$ nodes to minimize a certain graph measure. In this article, we propose a new Extremal Optimization-based approach, the Pseudo-Deterministic Noisy Extremal Optimization (PDNEO) algorithm, to solve the Critical Node Detection variant in which the pairwise connectivity is minimized. PDNEO uses an adaptive pseudo-deterministic parameter to switch between random nodes and articulation points during the search, as (...)
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  47.  20
    Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm.Hongshu Bao & Xiang Yao - 2020 - Complexity 2020:1-11.
    In recent years, with the rapid development of computer storage capabilities and network transmission capabilities, users can easily share their own video and image information on social networking sites, and the amount of multimedia data on the network is rapidly increasing. With the continuous increase of the amount of data in the network, the establishment of effective automated data management methods and search methods has become an increasingly urgent need. This paper proposes a retrieval method of human motion data (...)
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  48.  15
    A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem.Yi Feng, Mengru Liu, Yuqian Zhang & Jinglin Wang - 2020 - Complexity 2020:1-19.
    Job shop scheduling problem is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm named dynamic opposite learning assisted (...)
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  49.  20
    Exploration Enhanced RPSO for Collaborative Multitarget Searching of Robotic Swarms.Jian Yang, Ruilin Xiong, Xinhao Xiang & Yuhui Shi - 2020 - Complexity 2020:1-12.
    Particle Swarm Optimization is an excellent population-based optimization algorithm. Meanwhile, because of its inspiration source and the velocity update feature, it is also widely used in the collaborative searching tasks for swarm robotics. One of the PSO-based models for robotic swarm searching tasks is Robotic PSO. It adds additional items for obstacle avoidance into standard PSO and has been applied to many single-target search tasks. However, due to PSO’s global optimization characteristics, it is easy to converge (...)
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    Illustration Design Model with Clustering Optimization Genetic Algorithm.Jing Liu, Qixing Chen & Xiaoying Tian - 2021 - Complexity 2021:1-10.
    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 (...)
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