Constrained optimization with an improved particle swarm optimization algorithm

被引:26
|
作者
Munoz Zavala, Angel E. [1 ]
Hernandez Aguirre, Arturo [1 ]
Villa Diharce, Enrique R. [1 ]
Botello Rionda, Salvador [1 ]
机构
[1] Ctr Res Math, Dept Comp Sci, Guanajuato, Mexico
关键词
Optimization techniques; Programming and algorithm theory; Variance;
D O I
10.1108/17563780810893482
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Purpose - The purpose of this paper is to present a new constrained optimization algorithm based on a particle swarm optimization (PSO) algorithm approach. Design/methodology/approach - This paper introduces a hybrid approach based on a modified ring neighborhood with two new perturbation operators designed to keep diversity. A constraint handling technique based on feasibility and sum of constraints violation is adopted. Also, a special technique to handle equality constraints is proposed. Findings - The paper shows that it is possible to improve PSO and keeping the advantages of its social interaction through a simple idea: perturbing the PSO memory. Research limitations/implications - The proposed algorithm shows a competitive performance against the state-of-the-art constrained optimization algorithms. Practical implications - The proposed algorithm can be used to solve single objective problems with linear or non-linear functions, and subject to both equality and inequality constraints which can be linear and non-linear. In this paper, it is applied to various engineering design problems, and for the solution of state-of-the-art benchmark problems. Originality/value - A new neighborhood structure for PSO algorithm is presented. Two perturbation operators to improve PSO algorithm are proposed. A special technique to handle equality constraints is proposed.
引用
收藏
页码:425 / 453
页数:29
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