Population topologies for particle swarm optimization and differential evolution

被引:143
作者
Lynn, Nandar [1 ]
Ali, Mostafa Z. [2 ]
Suganthan, Ponnuthurai Nagaratnam [1 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
[2] Jordan Univ Sci & Technol, Irbid, Jordan
关键词
Particle swarm optimization; Differential evolution; Optimization; Population topology; Social network; Cellular; Distributed; Static; Dynamic; Ring; Wheels; Random; von Neumann; Star; Hierarchical; Niching; Multiswann; Subswarm; Subpopulation; Heterogeneous; NUMERICAL OPTIMIZATION; DIRECTION INFORMATION; GENETIC ALGORITHM; NEIGHBORHOOD; ENSEMBLE; PARAMETERS; CONVERGENCE; MIGRATION; SEARCH; MODELS;
D O I
10.1016/j.swevo.2017.11.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Over the last few decades, many population-based swarm and evolutionary algorithms were introduced in the literature. It is well known that population topology or sociometry plays an important role in improving the performance of population-based optimization algorithms by enhancing population diversity when solving multiobjective and multimodal problems. Many population structures and population topologies were developed for particle swarm optimization and differential evolutionary algorithms. Therefore, a comprehensive review of population topologies developed for PSO and DE is carried out in this paper. We anticipate that this survey will inspire researchers to integrate the population topologies into other nature inspired algorithms and to develop novel population topologies for improving the performances of population-based optimization algorithms for solving single objective optimization, multiobjective optimization and other classes of optimization problems.
引用
收藏
页码:24 / 35
页数:12
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