Constrained optimization with an improved particle swarm optimization algorithm

Abstract

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.