'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIt is particularly challenging for evolutionary algorithms to quickly converge to the Pareto front in large-scale multi-objective optimization. To tackle this problem, this paper proposes a large-scale multi-objective evolutionary algorithm assisted by some selected individuals generated by directed sampling. At each generation, a set of individuals closer to the ideal point are chosen for performing a directed sampling in the decision space, and those non-dominated ones of the sampled solutions are used to assist the reproduction to improve the convergence in evolutionary large-scale multi-objective optimization. In addition, elitist non-dominated sorting is adopted complementarily for environmental selection with a reference vector based method in order to maintain diversity of the population. Our experimental results show that the proposed algorithm is highly competitive on large-scale multi-objective optimization test problems with up to 5000 decision variables compared to five state-of-the-art multi-objective evolutionary algorithms.National Natural Science Foundation of China), Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi ProvinceShanxi Province Science Foundation for YouthsShanxi Science and Technology Innovation project for Excellent TalentsPostgraduate Education Innovation Project of Shanxi ProvinceChina Scholarship Council (CSC
