Two cooperative constraint handling techniques with an external archive for constrained multi-objective optimization

Constrained multi-objective problems are difficult for researchers to solve because they contain infeasible regions. To address this issue, this paper proposes two cooperative constraint handling techniques that use an external archive. First, two constraint handling techniques, i.e., the penalty function and the constrained dominance principle, are embedded in multi-objective optimization algorithms and work cooperatively on two populations to increase population diversity. Then, an external archive is designed to preserve high-quality solutions that strike a good balance between objectives, values, and constraints throughout the evolution process. Finally, comprehensive experiments are conducted to validate the performance of the proposed algorithm, and seven state-of-the-art constrained multi-objective optimization algorithms are used to compare three test suites and ten real-world problems. The experimental results demonstrate that the proposed algorithm can achieve competitive performance in solving various constrained multi-objective problems. Additionally, the results show that cooperative constraint handling techniques are more robust than single constraint handling methods.