CenPSO: A Novel Center-based Particle Swarm Optimization Algorithm for Large-scale Optimization

被引：15

作者：

Mousavirad, Seyed Jalaleddin ^{[1
]}

Rahnamayan, Shahryar ^{[2
]}

机构：

[1] Sabzevar Univ New Technol, Fac Engn, Sabzevar, Iran

[2] Ontario Tech Univ, Dept Elect Comp & Software Engn, Nat Inspired Computat Intelligence NICI Lab, Oshawa, ON, Canada

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2020年

关键词：

Particle swarm optimization; Center-based sampling; Optimization; Velocity; LSGO; Center-based PSO; DIFFERENTIAL EVOLUTION;

D O I：

10.1109/smc42975.2020.9283143

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Particle swarm optimization (PSO) has demonstrated a promising performance for solving challenging optimization problems, but its performance in solving large-scale optimization problems (LSGO) has drastically decreased. In the canonical PSO, velocity has a significant effect on the performance of PSO, which is updated based on cognitive and social factors. It can help particles to share information effectively. In this paper, a center-based velocity is proposed in which a new component, named opening "center of gravity factor", is added to velocity update rule to propose the center-based PSO (CenPSO). Center of gravity factor benefits from center-based sampling strategy, a new direction in population-based metaheuristics, especially to tackle LSGOs. The proposed method is evaluated on two benchmark functions, namely, CEC2010 and CEC2017, with dimensions 100 and 1000. The experimental results verify that CenPSO is significantly better than PSO over the majority of benchmark functions.

引用

页码：2066 / 2071

页数：6

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