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Swarm and Evolutionary Computation 中科院2区 JCR:Q1 SCIE EI PubMed JST
发文量 2,058
被引量 93,418
影响因子(2025版) 8.475

To tackle complex real world problems, scientists have been looking into natural processes and creatures - both as model and metaphor - for years. Optimization is at the heart of many natural processes including Darwinian evolution, social group behavior and foraging strategies. Over the last few decades, there has been remarkable growth in the field of nature-inspired search and optimization algorithms. Currently these techniques are applied to a variety of problems, ranging from scientific research to industry and commerce. The two main families of algorithms that primarily constitute this field today are the evolutionary computing methods and the swarm intelligence algorithms. Although both families of algorithms are generally dedicated towards solving search and optimization problems, they are certainly not equivalent, and each has its own distinguishing features. Reinforcing each other's performance makes powerful hybrid algorithms capable of solving many intractable search and optimization problems.

  • 主办单位： ELSEVIER
  • 出版地区： AMSTERDAM
  • 出版周期： 双月刊
  • 别名： SWARM EVOL COMPUT;Swarm Evol. Comput.;群体与进化计算;SWARM AND EVOLUTIONARY COMPUTATION
  • 国际标准连续出版物号/电子版： ISSN 2210-6502 / EISSN 2210-6510
  • 创刊时间： 2011年
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Particle Swarm OptimizationEvolutionary AlgorithmDifferential EvolutionGenetic AlgorithmMany-objective OptimizationGlobal OptimizationMulti-Objective Evolutionary AlgorithmEvolutionary ComputationMultiobjective OptimizationLocal SearchSwarm IntelligenceOptimization AlgorithmPareto FrontAnt Colony OptimizationDifferential Evolution AlgorithmMulti-objectiveArtificial Bee ColonyMemetic AlgorithmOptimization AlgorithmsObjective Space

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