Dynamic multi-objective differential evolution algorithm based on the information of evolution progress
The multi-objective differential evolution (MODE) algorithm is an effective method to solve multi-objective optimization problems. However, in the absence of any information of evolution progress, the optimization strategy of the MODE algorithm still appears as an open problem. In this paper, a dyna...
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| Vydané v: | Science China. Technological sciences Ročník 64; číslo 8; s. 1676 - 1689 |
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| Jazyk: | English |
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Beijing
Science China Press
01.08.2021
Springer Nature B.V |
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| ISSN: | 1674-7321, 1869-1900 |
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| Abstract | The multi-objective differential evolution (MODE) algorithm is an effective method to solve multi-objective optimization problems. However, in the absence of any information of evolution progress, the optimization strategy of the MODE algorithm still appears as an open problem. In this paper, a dynamic multi-objective differential evolution algorithm, based on the information of evolution progress (DMODE-IEP), is developed to improve the optimization performance. The main contributions of DMODE-IEP are as follows. First, the information of evolution progress, using the fitness values, is proposed to describe the evolution progress of MODE. Second, the dynamic adjustment mechanisms of evolution parameter values, mutation strategies and selection parameter value based on the information of evolution progress, are designed to balance the global exploration ability and the local exploitation ability. Third, the convergence of DMODE-IEP is proved using the probability theory. Finally, the testing results on the standard multi-objective optimization problem and the wastewater treatment process verify that the optimization effect of DMODE-IEP algorithm is superior to the other compared state-of-the-art multi-objective optimization algorithms, including the quality of the solutions, and the optimization speed of the algorithm. |
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| AbstractList | The multi-objective differential evolution (MODE) algorithm is an effective method to solve multi-objective optimization problems. However, in the absence of any information of evolution progress, the optimization strategy of the MODE algorithm still appears as an open problem. In this paper, a dynamic multi-objective differential evolution algorithm, based on the information of evolution progress (DMODE-IEP), is developed to improve the optimization performance. The main contributions of DMODE-IEP are as follows. First, the information of evolution progress, using the fitness values, is proposed to describe the evolution progress of MODE. Second, the dynamic adjustment mechanisms of evolution parameter values, mutation strategies and selection parameter value based on the information of evolution progress, are designed to balance the global exploration ability and the local exploitation ability. Third, the convergence of DMODE-IEP is proved using the probability theory. Finally, the testing results on the standard multi-objective optimization problem and the wastewater treatment process verify that the optimization effect of DMODE-IEP algorithm is superior to the other compared state-of-the-art multi-objective optimization algorithms, including the quality of the solutions, and the optimization speed of the algorithm. |
| Author | Wu, YiLin Wang, Pu Han, HongGui Liu, Zheng Hou, Ying |
| Author_xml | – sequence: 1 givenname: Ying surname: Hou fullname: Hou, Ying email: houying@bjut.edu.cn organization: Faculty of Information Technology, Beijing University of Technology, Engineering Research Center of Digital Community, Ministry of Education – sequence: 2 givenname: YiLin surname: Wu fullname: Wu, YiLin organization: Faculty of Information Technology, Beijing University of Technology, Engineering Research Center of Digital Community, Ministry of Education – sequence: 3 givenname: Zheng surname: Liu fullname: Liu, Zheng organization: Faculty of Information Technology, Beijing University of Technology – sequence: 4 givenname: HongGui surname: Han fullname: Han, HongGui organization: Faculty of Information Technology, Beijing University of Technology, Engineering Research Center of Digital Community, Ministry of Education – sequence: 5 givenname: Pu surname: Wang fullname: Wang, Pu organization: Faculty of Information Technology, Beijing University of Technology, Engineering Research Center of Digital Community, Ministry of Education |
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| Cites_doi | 10.1016/j.ins.2019.08.040 10.1109/TEVC.2013.2285016 10.1016/j.swevo.2011.02.002 10.1109/TEVC.2019.2896967 10.1016/j.asoc.2017.05.062 10.1162/evco.2010.18.1.18105 10.1016/j.neucom.2017.08.059 10.1007/s00500-016-2418-1 10.1109/TEVC.2014.2308305 10.1109/TCYB.2018.2849343 10.1016/j.swevo.2018.10.006 10.1016/j.swevo.2018.06.010 10.1007/s11432-018-9720-6 10.1109/TEVC.2010.2059031 10.1162/evco.1994.2.3.221 10.1007/s40747-017-0039-7 10.1016/j.cor.2009.02.006 10.1016/j.chemolab.2014.05.007 10.1109/TEVC.2014.2332878 10.1007/s00521-016-2426-1 10.1016/j.swevo.2020.100666 10.1007/s10845-017-1294-6 10.1007/s00500-020-04732-y 10.1016/j.ins.2016.07.009 10.1007/s00521-017-3212-4 10.1109/4235.996017 |
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| Title | Dynamic multi-objective differential evolution algorithm based on the information of evolution progress |
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