A naive particle swarm algorithm for constrained optimization problems

In this paper, a naive particle swarm algorithm for constrained optimization problems is proposed. Also, a direct constraint handling method is adopted, which is more direct and easier to be implemented in comparison with other constraint handling methods, such as penalty function, etc. The experimental results on benchmark problems show that the proposed algorithm can solve constrained optimization problems effectively and has a robust performance.

1 INTRODUCTION

As a population-based search method, particle swarm optimization (PSO) has achieved tremendous successes in optimization of continuous nonlinear problems, since it was introduced by James Kennedy and Russell Eberhart in 1995[1]. For no more than 20 years, PSO has been used across a wide range of applications [2][3], such as image and video analysis, design and restructuring of electricity networks, control, antenna design, electronics and electromagnetics, and so on. While the standard PSO is applied to solve unconstrained single objective problems, its variations with necessary extension of basic idea are applicable to discrete optimization problems, constrained optimization problems and multi-objective optimization problems, etc.