Engineering Design Optimization Using Hybrid (DE-PSO-DE) Algorithm

Abstract

In this paper, a novel hybrid intelligent algorithm, integrating with differential evolution (DE) and particle swarm optimization (PSO), is proposed. Initially, all individual in the population are divided into three groups (in increasing order of function value): inferior group, mid-group, and superior group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. The proposed method uses DE-PSO-DE, then it is denoted by DPD. At present, many mutation strategies of DE are reported. Every mutation strategy has its own pros and cons, so which one of them should be selected is critical for DE. Therefore, over 8 mutation strategies, the best one is investigated for both DEs used in DPD. Moreover, two strategies, namely elitism (to retain the best obtained values so far) and Non-redundant search (to improve the solution quality), have been employed in DPD cycle. Combination of 8 mutation strategies generated 64 different variants of DPD. Top 4 DPDs are investigated through solving a set of constrained benchmark functions. Based on the ‘performance,’ best DPD is reported and further used in solving engineering design problem.