Enhanced auxiliary population search for diversity improvement of constrained multiobjective coevolutionary optimization

The challenge of solving constrained multiobjective optimization problems (CMOPs) is to obtain solution sets that satisfy all constraints and have global optimal convergence and diversity. However, maintaining the distribution of solutions in small and discrete feasible regions while balancing convergence and feasibility has become increasingly challenging when optimizing many complex CMOPs. To address this issue, this paper proposes an enhanced auxiliary population search, EAPS, based on coevolutionary optimization to improve the distribution of the main population in solving complex CMOPs. Specifically, the auxiliary population adopts a new aggregation method under the decomposition framework to evolve independently without considering any constraints and provides favorable diversity information uni-directionally to the main population. The proposed algorithm is compared with five state-of-the-art constrained multiobjective algorithms on four well-known constraint test suites. The experimental results demonstrate that the proposed are competitive or comparable with the comparison algorithms.

Automated summary

This paper proposes an enhanced auxiliary population search algorithm (EAPS) to solve complex constrained multiobjective optimization problems. The algorithm improves the distribution of the main population by introducing an auxiliary population and provides favorable diversity information while balancing the convergence and feasibility of solutions.