Constrained optimization with an improved particle swarm optimization algorithm

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 linea...

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