Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
This addresses constrained multi-objective optimization problems, which are common in engineering and design, but the approach appears incremental as it builds on existing evolutionary methods.
The paper tackled the problem of balancing convergence, diversity, and feasibility in constrained multi-objective optimization by proposing a two-archive evolutionary algorithm, which demonstrated competitiveness against five state-of-the-art methods in experiments on benchmarks and a real-world case.
When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, two-archive evolutionary algorithm, for constrained multi-objective optimization. It maintains two co-evolving populations simultaneously: one, denoted as convergence archive, is the driving force to push the population toward the Pareto front; the other one, denoted as diversity archive, mainly tends to maintain the population diversity. In particular, to complement the behavior of the convergence archive and provide as much diversified information as possible, the diversity archive aims at exploring areas under-exploited by the convergence archive including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, comparing to five state-of-the-art constrained evolutionary multi-objective optimizers.