Hybrid driven strategy for constrained evolutionary multi-objective optimization

Abstract

In the constrained multi-objective optimization problems, the pursuit of feasibility could improve convergence but will lead to the loss of diversity. For optimization algorithm, balancing the weight between convergence and diversity dynamically is a challenge, especially in problems with low proportion of feasible regions. In this paper, a constrained multi-objective optimization algorithm is proposed based on a hybrid driven strategy to enhance both the feasibility and diversity performance of the approximate Pareto solutions. The proposed algorithm contains two archives, that one is driven by feasibility information and the other is driven by diversity information. A self-adaptive archive selection mechanism and a conditional tournament selection strategy are proposed to provide mating parent solutions according to the evolutionary stage. Moreover, in the update of the feasibility archive, an evolutionary direction prediction mechanism is proposed and adopted to improve the evolutionary efficiency. Compared to four other multi-objective algorithms on three benchmark suits of different types, the performance of the proposed algorithm is better than the peer algorithms, especially in large-infeasible-regions multi-objective optimization problems.