Dynamic multi-objective optimization algorithm based decomposition and preference
Most of the existing dynamic multi-objective evolutionary algorithms (DMOEAs) are effective, which focuses on searching for the approximation of Pareto-optimal front (POF) with well-distributed in handling dynamic multi-objective optimization problems (DMOPs). Nevertheless, in real-world scenarios,...
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| Vydáno v: | Information sciences Ročník 571; s. 175 - 190 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.09.2021
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Most of the existing dynamic multi-objective evolutionary algorithms (DMOEAs) are effective, which focuses on searching for the approximation of Pareto-optimal front (POF) with well-distributed in handling dynamic multi-objective optimization problems (DMOPs). Nevertheless, in real-world scenarios, the decision maker (DM) may be only interested in a portion of the corresponding POF (i.e., the region of interest) for different instances, rather than the whole POF. Consequently, a novel DMOEA based decomposition and preference (DACP) is proposed, which incorporates the preference of DM into the dynamic search process and tracks a subset of Pareto-optimal set (POS) approximation with respect to the region of interest (ROI). Due to the presence of dynamics, the ROI, which is defined in which DM gives both the preference point and the neighborhood size, may be changing with time-varying DMOPs. Consequently, our algorithm moves the well-distributed reference points, which are located in the neighborhood range, to around the preference point to lead the evolution of the whole population. When a change occurs, a novel strategy is performed for responding to the current change. Particularly, the population will be reinitialized according to a promising direction obtained by letting a few solutions evolve independently for a short time. Comprehensive experiments show that this approach is very competitivecompared with state-of-the-art methods. |
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| AbstractList | Most of the existing dynamic multi-objective evolutionary algorithms (DMOEAs) are effective, which focuses on searching for the approximation of Pareto-optimal front (POF) with well-distributed in handling dynamic multi-objective optimization problems (DMOPs). Nevertheless, in real-world scenarios, the decision maker (DM) may be only interested in a portion of the corresponding POF (i.e., the region of interest) for different instances, rather than the whole POF. Consequently, a novel DMOEA based decomposition and preference (DACP) is proposed, which incorporates the preference of DM into the dynamic search process and tracks a subset of Pareto-optimal set (POS) approximation with respect to the region of interest (ROI). Due to the presence of dynamics, the ROI, which is defined in which DM gives both the preference point and the neighborhood size, may be changing with time-varying DMOPs. Consequently, our algorithm moves the well-distributed reference points, which are located in the neighborhood range, to around the preference point to lead the evolution of the whole population. When a change occurs, a novel strategy is performed for responding to the current change. Particularly, the population will be reinitialized according to a promising direction obtained by letting a few solutions evolve independently for a short time. Comprehensive experiments show that this approach is very competitivecompared with state-of-the-art methods. |
| Author | Yang, Shengxiang Zou, Juan Hu, Yaru Zheng, Jinhua Jiang, Shouyong |
| Author_xml | – sequence: 1 givenname: Yaru surname: Hu fullname: Hu, Yaru email: huyaru1199@gmail.com organization: The Department of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China – sequence: 2 givenname: Jinhua surname: Zheng fullname: Zheng, Jinhua email: jhzheng@xtu.edu.cn organization: Key Laboratory of Intelligent Computing and Information Processing (Ministry of Education), Xiangtan University, Xiangtan, Hunan 411105, China – sequence: 3 givenname: Juan surname: Zou fullname: Zou, Juan email: zoujuan@xtu.edu.cn organization: Key Laboratory of Intelligent Computing and Information Processing (Ministry of Education), Xiangtan University, Xiangtan, Hunan 411105, China – sequence: 4 givenname: Shouyong surname: Jiang fullname: Jiang, Shouyong email: math4neu@gmail.com organization: School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK – sequence: 5 givenname: Shengxiang surname: Yang fullname: Yang, Shengxiang email: syang@dmu.ac.uk organization: School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK |
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| Keywords | Dynamic multi-objective evolutionary algorithms (DMOEAs) Reference points Changing preference point The region of interest (ROI) |
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