A Hybrid Multi-objective Evolutionary Algorithm and Its Application in Component-based Product Design
Xiangwei Zheng, Huichuan Duan, Hong Liu · 2007
Component-based product design usually appears as a multi-objective optimization problem (MOP). Traditional methods solving MOPs are robust and have proven their effectiveness in handling many classes of optimization problems. However, such techniques can encounter difficulties such as getting trapped in local minima, increasing computational complexity, and not being applicable to certain classes of objective functions. Multi-Objective Evolutionary Algorithms (MOEAs) can overcome these disadvantages and have shown great potentials to solve MOPs. In this paper, an h-MOEA is proposed by employing effective strategies from evolutionary computation, which is suitable for solving the MOP in design optimization and can generate more diverse solutions in an accepted time span. Then, the effectiveness and correctness of h-MOEA is verified using several popular benchmark functions. Also, a prototype is developed and used in component-based product design optimization. Finally, the optimization results of a product design case are shown in detail.