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dc.contributor.author | Zuo, Mingcheng![]() |
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dc.contributor.author | Gong, Dunwei![]() |
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dc.contributor.author | Wang, Yan![]() |
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dc.contributor.author | Ye, Xianming![]() |
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dc.contributor.author | Zeng, Bo![]() |
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dc.contributor.author | Meng, Fanlin![]() |
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dc.date.accessioned | 2023-03-23T05:08:36Z | |
dc.date.available | 2023-03-23T05:08:36Z | |
dc.date.issued | 2024-02 | |
dc.description.abstract | Various 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_US |
dc.description.department | Electrical, Electronic and Computer Engineering | en_US |
dc.description.librarian | hj2023 | en_US |
dc.description.sponsorship | The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central Universities and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing. | en_US |
dc.description.uri | http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235 | en_US |
dc.identifier.citation | Zuo, 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. | en_US |
dc.identifier.issn | 1089-778X (online) | |
dc.identifier.other | 10.1109/TEVC.2023.3243109 | |
dc.identifier.uri | http://hdl.handle.net/2263/90178 | |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. | en_US |
dc.subject | Integrated coal mine energy system | en_US |
dc.subject | Process knowledge | en_US |
dc.subject | Autonomy | en_US |
dc.subject | Evolutionary optimization | en_US |
dc.subject | Constrained multi-objective optimization | en_US |
dc.subject | Optimization | en_US |
dc.subject | Optimization methods | en_US |
dc.subject | Search problems | en_US |
dc.subject | Space vehicles | en_US |
dc.subject | Evolutionary computation | en_US |
dc.subject | Sociology | en_US |
dc.subject | Statistics | en_US |
dc.title | Process knowledge-guided autonomous evolutionary optimization for constrained multi-objective problems | en_US |
dc.type | Postprint Article | en_US |