A simple multi-population evolutionary algorithm using PSO strategy for constrained engineering design optimization

Abstract

This paper presents a new approach for solving constrained optimization problems, which is named as the simple multi-population evolutionary algorithm using PSO strategy (PSO-SMEA). In the PSO-SMEA, a constrained optimization problem is converted into several unconstrained optimization problems by parallel evolutions of two populations, which are dynamically formed. At the beginning of each iteration, individuals in feasible regions form the feasible population, and individuals in infeasible regions form the infeasible population. In the PSO-SMEA, the PSO strategy performed on the feasible population aims to find the minimal objective function value, and the PSO strategy performed on the infeasible population aims to find the minimal objective function value or the lowest constraints violation according to the ratio of the feasible population size to the whole population size in the current generation. The PSO-SMEA is applied to solving four well-studied constrained engineering design optimization problems, and the results show that the PSO-SMEA is competitive when compared with two other constrained optimization algorithms.