History-Assisted Two-State Auxiliary Task Collaboration Approach for Dynamic Constrained Multiobjective Optimization
提出一种历史辅助的双状态辅助任务协作方法，通过从历史环境中提取相似环境作为辅助任务，并设计两状态选择策略，提升动态约束多目标优化的进化过程，在基准测试和矿物处理实际问题中验证了有效性。
Dynamic constrained multiobjective optimization problems (DCMOPs) are widely encountered in real-world applications and have attracted increasing attention in the evolutionary computation community. Existing studies primarily focus on the population initialization, but disregard to improve the evolution process. In DCMOPs, the knowledge is undoubtedly more abundant as the existence of historical environments. Therefore, extracting and utilizing useful knowledge from historical environments can further improve the evolution process. In this article, a history-assisted two-state auxiliary task collaboration approach is proposed to solve DCMOPs by conducting a more effective auxiliary task. Specifically, the algorithm indicates that constrained Pareto-optimal front (CPF) in similar historical environments is more suitable as an auxiliary task for current evolution, as it is closer to the current CPF. To identify the most suitable environment from extensive historical environments, a novel prediction-based similar environment identification method is proposed for the auxiliary task. To fully utilize the novel auxiliary task, an experience-driven two-state environmental selection strategy is proposed, which conditionally considers both historical and current information. In this strategy, individuals in state A are inclined to promote toward the historical CPF while disregarding the dominance relationship or constraints, to help cross infeasible region and approach CPF rapidly. For those individuals in state B, dominance relationship is taken into consideration, further bringing auxiliary task closer to the main task. The superiority of the proposed algorithm has been comprehensively demonstrated by experimental comparison results on a variety of benchmark test problems and a real-world problem with raw ore allocation in mineral processing.