Feasibility-guided search and prediction for dynamic constrained multiobjective evolutionary optimization
Lisha Dong, Qianhui Wang, Q. Liu, Junkai Ji, Ka‐Chun Wong, Qiuzhen Lin · Swarm and Evolutionary Computation · 2025
Dynamic constrained multiobjective optimization problems (DCMOPs) are characterized by the variations of both objectives and constraints over time, posing two main challenges: (1) balancing feasibility, convergence, and diversity in the evolutionary search and (2) generating an effective initial population for new environments. To address these problems, this paper proposes a dynamic constrained multiobjective evolutionary algorithm with feasibility-guided search and prediction (called FGSP), which integrates a feasibility-guided evolutionary search (FGES) and a feasible information guidance prediction (FIGP). Specifically, FGES adaptively adjusts evolutionary strategies by monitoring the proportion of infeasible solutions and a time-dependent tolerance threshold for infeasibility, such that it can perform exploration without constraints to navigate through large infeasible regions and conduct feasibility-driven exploitation to refine solutions near the constrained Pareto front, thereby balancing convergence, feasibility, and diversity. Concurrently, FIGP utilizes an artificial neural network trained on historically feasible solutions to predict a high-quality initial population for new environments, significantly accelerating adaptation to dynamic changes via pattern learned from past environments. After comparing the proposed FGSP with five state-of-the-art algorithms on the latest benchmark problems and one real-world problem, the experimental results validate the effectiveness of FGSP in obtaining feasible non-dominated solutions.