Development and investigation of efficient GA/PSO-hybrid algorithm applicable to real-world design optimization

Abstract

Genetic Algorithms (GAs) generally maintain diverse solutions of good quality in multi-objective problems, while Particle Swarm Optimization (PSO) shows rapid convergence to the optimum solution. Previous studies indicated that search abilities can be improved by simply coupling these two algorithms; GA compensates for the low diversity of PSO, while PSO compensates for the high computational costs of GA. In this study, the configurations of the two methods when used in a fully coupled hybrid algorithm were investigated to achieve an improvement in diversity and convergence simultaneously for application to real-world engineering design problems. The new hybrid algorithm was validated using standard test function problems, and it was demonstrated that the new hybrid algorithm showed better performance than the simply coupled hybrid algorithm, as well as both pure GA and pure PSO. Especially, the new hybrid algorithm shows robust search ability regardless of initial population selection. This feature is very important in real-world engineering design problems, which do not allow multiple optimization runs to be implemented due to heavy computational costs. The new method was applied to optimization of a diesel engine combustion chamber to reduce exhaust emissions, such as NOx and soot. The results demonstrated the applicability of the present method to real-world design problems. In addition, important geometry design variables controlling the emission performance were investigated to obtain useful knowledge about low emission diesel engine design.