An improved multi-objective evolutionary algorithm based on environmental and history information

Proximity and diversity are two basic issues in multi-objective optimization problems. However, it is hard to optimize them simultaneously, especially when tackling problems with complicated Pareto fronts and Pareto sets. To make a better performance of multi-objective optimization evolutionary algorithm, the environmental information and history information are used to generate better offsprings. The conception of locality and reference front is introduced to improve the diversity. Adaptation mechanism of evolutionary operator is proposed to solve searching issue during different stages in evolutionary process. Based on these improvement, an improved multi-objective evolutionary algorithm based on environmental and history information (MOEA-EHI) is presented. The performance of our proposed method is validated based inverted generation distance (IGD) and compared with three state-of-the-art algorithms on a number of unconstrained benchmark problems. Empirical results fully demonstrate the superiority of our proposed method on complicated benchmarks.