Zuo, MingchengGong, DunweiWang, YanYe, XianmingZeng, BoMeng, Fanlin2023-03-232023-03-232024-02Zuo, M., Gong, D., Wang, Y. et al. 'Process knowledge-guided autonomous evolutionary optimization for constrained multi-objective problems', IEEE Transactions on Evolutionary Computation, vol. 28, no. 1, pp. 193-207, Feb. 2024, doi: 10.1109/TEVC.2023.3243109.1089-778X (online)10.1109/TEVC.2023.3243109http://hdl.handle.net/2263/90178Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, 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 constrained multi-objective optimization problems.en© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.Integrated coal mine energy systemProcess knowledgeAutonomyEvolutionary optimizationConstrained multi-objective optimizationOptimizationOptimization methodsSearch problemsSpace vehiclesEvolutionary computationSociologyStatisticsPostprint Article