An Adaptive Two-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization Problems

Striking a balance between objective optimization and constraint satisfaction is essential for solving constrained multi-objective problems (CMOPs). Nevertheless, most existing evolutionary algorithms face significant challenges on CMOPs with intricate infeasible regions. To tackle these challenges, this paper proposes an adaptive two-population algorithm, named ATEA, which dynamically exploits promising information under solutions to facilitate satisfaction. Specifically, collaboration mechanism designed the unconstrained Pareto front search search. Moreover, handling strategy presented reasonably deploy resources. Furthermore, infeasibility-based environmental selection elitist feasibility-based are developed two populations break through complex barriers enhance pressure, respectively. Comparison experimental results of ATEA five state-of-the-art 33 benchmark test 4 real-word demonstrate that performs competitively chosen designs.