A new two-stage based evolutionary algorithm for solving multi-objective optimization problems.

It is a challenge to balance the convergence and the diversity in multi-objective optimization problems. In this paper, a new two-stage based evolutionary algorithm (MOEA/TS) is proposed, where the convergence and the diversity are handled in two independent phases. In the first stage, the convergence is accelerated by using the gradient information of constrained sub-problems. In the second stage, the diversity is improved by adopting the dominance based multi-objective evolutionary algorithm. The comparative experiments are presented in terms of two performance indicators for benchmark test problems. The results indicates that MOEA/TS has the competitive performance. [ABSTRACT FROM AUTHOR]