Multi-parent Mutation in Differential Evolution for Multi-objective Optimization
Youyun Ao, Hongqin Chi · 2009
Differential evolution (DE) is a fast and effective computing method and technique. In differential evolution for global optimization, mutation plays a key role in the performance and there are several mutation variants, which have been widely used in both benchmark test functions and real-world applications. However, most of these mutation variants can only generate one offspring in one mutation operation. In order to make the best of the information of multiple parents in the process of mutation, this paper proposes a multi-parent mutation, and then extends differential evolution with the multi-parent mutation to handle multi-objective optimization problems. Simulation results on a set of test functions show that the proposed approaches can improve the search performance.