Constrained optimization with an improved particle swarm optimization algorithm

• Published in: International journal of intelligent computing and cybernetics Vol. 1; no. 3; pp. 425 - 453
• Main Authors: Muñoz Zavala, Angel E., Hernández Aguirre, Arturo, Villa Diharce, Enrique R., Botello Rionda, Salvador
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 22.08.2008
• Subjects: Algorithms; Bias; Computer programming; Constraints; Genetic algorithms; Handles; Mathematical models; Mutation; Neighborhoods; Nonlinearity; Optimization; Optimization techniques; Population; State of the art; Studies; Variance analysis; Velocity; Optimization techniques; Programming and algorithm theory; Variance
• ISSN: 1756-378X; 1756-3798
• DOI: 10.1108/17563780810893482

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.