An Effective Hybrid DE with PSO for Constrained Engineering Design Problem

Abstract

Many engineering design problems can be formulated as constrained optimization problems. Solving constrained engineering design problems via evolutionary algorithms has attracted increasing attention in the past decade. So far, penalty function methods have been the most popular methods for constrained optimization due to their simplicity and easy implementation. However, it is often not easy to set suitable penalty factors. This paper proposes an alternative hybrid approach (namely DPD) to efficient solving for constrained engineering design optimization problems combining by differential evolution (DE) and particle swarm optimization (PSO) algorithms. DPD is based on tri-break-up concept of population. Initially all individual in the population are divided into three groups – Inferior Group, Mid Group and Superior Group; according to their increasing order of function value. Initially the suitable mutation operators for both DEs used in DPD are investigated. The investigated mutation combination for DEs in DPD algorithm is shown to enhance the local search ability of the basic DE. Moreover, two strategies Elitism and Non-redundant search improve the quality of the solutions in the memory of each individual. Under the guidance of the bracket operator penalty (exterior penalty), the algorithm quickly finds better feasible solution. This algorithm has been applied to two constrained engineering optimization problems reported in the specialized literature. DPD compared with respect to algorithms representative of the state-of-the-art in the area. The results indicate that the DPD is a powerful optimization technique that may yield better solutions to engineering problems.