A Preliminary Study of Improving Evolutionary Multi-Objective Optimization via Knowledge Transfer from Single-Objective Problems
Lingyu Huang, Liang Feng, Handing Wang, Yaqing Hou, Kai Liu, Chao Chen · 2020
In the last decades, evolutionary algorithms (EAs) have demonstrated strong search capabilities in solving multi-objective optimization problems (MOPs). To improve the search performance of EAs, as problems seldom exist in isolation, transferring knowledge from related problems have attracted considerable attentions in recent years. In this paper, we present a preliminary study to enhance existing evolutionary algorithms (MOEAs) by transferring knowledge from the process of solving the single objectives involved in a given MOP of interest. As the single objectives are the objectives of the MOP, they naturally share great similarity with the given MOP, which thus could yield useful traits for enhancing the problem-solving of the MOP. To the best of our knowledge, this work severs as the first attempt to improve evolutionary multi-objective optimization via transferring knowledge from single objective problems. To evaluate the performance of the proposed method, empirical studies using a popular MOEA, i.e., NSGAII, on commonly used multi-objective benchmarks are conducted. The obtained results confirmed the efficacy of the proposed method in terms of both convergence speed and solution quality.