A knee-point-based evolutionary algorithm using weighted subpopulation for many-objective optimization

Abstract

Abstract Among many-objective optimization problems (MaOPs), the proportion of nondominated solutions is too large to distinguish among different solutions, which is a great obstacle in the process of solving MaOPs. Thus, this paper proposes an algorithm which uses a weighted subpopulation knee point. The weight is used to divide the whole population into a number of subpopulations, and the knee point of each subpopulation guides other solutions to search. Additionally, the convergence of the knee point approach can be exploited, and the subpopulation-based approach improves performance by improving the diversity of the evolutionary algorithm. Therefore, these advantages can make the algorithm suitable for solving MaOPs. Experimental results show that the proposed algorithm performs better on most test problems than six other state-of-the-art many-objective evolutionary algorithms.