A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing
[Display omitted] ► A novel hybrid multiobjective evolutionary optimization strategy is proposed. ► Evolutionary algorithms can be enhanced to track the moving of Pareto Front. ► The hybrid algorithms perform better than pure algorithms in dynamic environments. The supply trajectory of electric powe...
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| Published in: | Applied soft computing Vol. 13; no. 5; pp. 2960 - 2969 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.05.2013
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | [Display omitted]
► A novel hybrid multiobjective evolutionary optimization strategy is proposed. ► Evolutionary algorithms can be enhanced to track the moving of Pareto Front. ► The hybrid algorithms perform better than pure algorithms in dynamic environments.
The supply trajectory of electric power for submerged arc magnesia furnace determines the yields and grade of magnesia grain during the manufacture process. As the two production targets (i.e., the yields and the grade of magnesia grain) are conflicting and the process is subject to changing conditions, the supply of electric power needs to be dynamically optimized to track the moving Pareto optimal set with time. A hybrid evolutionary multiobjective optimization strategy is proposed to address the dynamic multiobjective optimization problem. The hybrid strategy is based on two techniques. The first one uses case-based reasoning to immediately generate good solutions to adjust the power supply once the environment changes, and then apply a multiobjective evolutionary algorithm to accurately solve the problem. The second one is to learn the case solutions to guide and promote the search of the evolutionary algorithm, and the best solutions found by the evolutionary algorithm can be used to update the case library to improve the accuracy of case-based reasoning in the following process. Due to the effectiveness of mutual promotion, the hybrid strategy can continuously adapt and search in dynamic environments. Two prominent multiobjective evolutionary algorithms are integrated into the hybrid strategy to solve the dynamic multiobjective power supply optimization problem. The results from a series of experiments show that the proposed hybrid algorithms perform better than their component multiobjective evolutionary algorithms for the tested problems. |
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| AbstractList | [Display omitted]
► A novel hybrid multiobjective evolutionary optimization strategy is proposed. ► Evolutionary algorithms can be enhanced to track the moving of Pareto Front. ► The hybrid algorithms perform better than pure algorithms in dynamic environments.
The supply trajectory of electric power for submerged arc magnesia furnace determines the yields and grade of magnesia grain during the manufacture process. As the two production targets (i.e., the yields and the grade of magnesia grain) are conflicting and the process is subject to changing conditions, the supply of electric power needs to be dynamically optimized to track the moving Pareto optimal set with time. A hybrid evolutionary multiobjective optimization strategy is proposed to address the dynamic multiobjective optimization problem. The hybrid strategy is based on two techniques. The first one uses case-based reasoning to immediately generate good solutions to adjust the power supply once the environment changes, and then apply a multiobjective evolutionary algorithm to accurately solve the problem. The second one is to learn the case solutions to guide and promote the search of the evolutionary algorithm, and the best solutions found by the evolutionary algorithm can be used to update the case library to improve the accuracy of case-based reasoning in the following process. Due to the effectiveness of mutual promotion, the hybrid strategy can continuously adapt and search in dynamic environments. Two prominent multiobjective evolutionary algorithms are integrated into the hybrid strategy to solve the dynamic multiobjective power supply optimization problem. The results from a series of experiments show that the proposed hybrid algorithms perform better than their component multiobjective evolutionary algorithms for the tested problems. |
| Author | Yang, Shengxiang Kong, Weijian Ding, Jinliang Chai, Tianyou |
| Author_xml | – sequence: 1 givenname: Weijian surname: Kong fullname: Kong, Weijian email: weijian.kong9@gmail.com organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, 110004 Shenyang, China – sequence: 2 givenname: Tianyou surname: Chai fullname: Chai, Tianyou organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, 110004 Shenyang, China – sequence: 3 givenname: Shengxiang surname: Yang fullname: Yang, Shengxiang organization: Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, United Kingdom – sequence: 4 givenname: Jinliang surname: Ding fullname: Ding, Jinliang organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, 110004 Shenyang, China |
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| Keywords | Dynamic multiobjective optimization Manufacture process of magnesia grain Multiobjective evolutionary algorithm Hybrid strategy Case-based reasoning |
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► A novel hybrid multiobjective evolutionary optimization strategy is proposed. ► Evolutionary algorithms can be enhanced to track the moving... |
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| SubjectTerms | Case-based reasoning Dynamic multiobjective optimization Hybrid strategy Manufacture process of magnesia grain Multiobjective evolutionary algorithm |
| Title | A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing |
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