Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems
提出一种过程知识引导的自主进化优化方法，通过评估不同求解策略对种群状态的影响并建立映射模型，自动推荐后续策略，可嵌入现有进化算法提升性能，在41个基准问题和30个煤矿综合能源系统调度问题中验证了有效性。
Various real-world problems can be attributed to constrained multiobjective optimization problems (CMOPs). Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for CMOPs. Given this, a process knowledge-guided constrained multiobjective autonomous evolutionary optimization method is proposed. First, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving CMOPs.