Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem
The collective decision optimization algorithm (CDOA) is a new stochastic population-based evolutionary algorithm which simulates the decision behavior of human. In this paper, a multiobjective collective decision optimization algorithm (MOCDOA) is first proposed to solve the environmental/economic...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1027193 |
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| _version_ | 1841524623217262592 |
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| author | Xinlin Xu Zhongbo Hu Qinghua Su Zenggang Xiong |
| author_facet | Xinlin Xu Zhongbo Hu Qinghua Su Zenggang Xiong |
| author_sort | Xinlin Xu |
| collection | DOAJ |
| description | The collective decision optimization algorithm (CDOA) is a new stochastic population-based evolutionary algorithm which simulates the decision behavior of human. In this paper, a multiobjective collective decision optimization algorithm (MOCDOA) is first proposed to solve the environmental/economic dispatch (EED) problem. MOCDOA uses three novel learning strategies, that is, a leader-updating strategy based on the maximum distance of each solution in an external archive, a wise random perturbation strategy based on the sparse mark around a leader, and a geometric center-updating strategy based on an extreme point. The proposed three learning strategies benefit the improvement of the uniformity and the diversity of Pareto optimal solutions. Several experiments have been carried out on the IEEE 30-bus 6-unit test system and 10-unit test system to investigate the performance of MOCDOA. In terms of extreme solutions, compromise solution, and three metrics (SP, HV, and CM), MOCDOA is compared with other existing multiobjective optimization algorithms. It is demonstrated that MOCDOA can generate the well-distributed and the high-quality Pareto optimal solutions for the EED problem and has the potential to solve the multiobjective optimization problems of other power systems. |
| format | Article |
| id | doaj-art-5e4fd2e9d4fb4e14965933a2f6200dc7 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5e4fd2e9d4fb4e14965933a2f6200dc72025-02-03T05:47:43ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/10271931027193Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch ProblemXinlin Xu0Zhongbo Hu1Qinghua Su2Zenggang Xiong3School of Information and Mathematics, Yangtze University, Jingzhou, Hubei, ChinaSchool of Information and Mathematics, Yangtze University, Jingzhou, Hubei, ChinaSchool of Information and Mathematics, Yangtze University, Jingzhou, Hubei, ChinaSchool of Computer and Information Science, Hubei Engineering University, Xiaogan, Hubei, ChinaThe collective decision optimization algorithm (CDOA) is a new stochastic population-based evolutionary algorithm which simulates the decision behavior of human. In this paper, a multiobjective collective decision optimization algorithm (MOCDOA) is first proposed to solve the environmental/economic dispatch (EED) problem. MOCDOA uses three novel learning strategies, that is, a leader-updating strategy based on the maximum distance of each solution in an external archive, a wise random perturbation strategy based on the sparse mark around a leader, and a geometric center-updating strategy based on an extreme point. The proposed three learning strategies benefit the improvement of the uniformity and the diversity of Pareto optimal solutions. Several experiments have been carried out on the IEEE 30-bus 6-unit test system and 10-unit test system to investigate the performance of MOCDOA. In terms of extreme solutions, compromise solution, and three metrics (SP, HV, and CM), MOCDOA is compared with other existing multiobjective optimization algorithms. It is demonstrated that MOCDOA can generate the well-distributed and the high-quality Pareto optimal solutions for the EED problem and has the potential to solve the multiobjective optimization problems of other power systems.http://dx.doi.org/10.1155/2018/1027193 |
| spellingShingle | Xinlin Xu Zhongbo Hu Qinghua Su Zenggang Xiong Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem Complexity |
| title | Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem |
| title_full | Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem |
| title_fullStr | Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem |
| title_full_unstemmed | Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem |
| title_short | Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem |
| title_sort | multiobjective collective decision optimization algorithm for economic emission dispatch problem |
| url | http://dx.doi.org/10.1155/2018/1027193 |
| work_keys_str_mv | AT xinlinxu multiobjectivecollectivedecisionoptimizationalgorithmforeconomicemissiondispatchproblem AT zhongbohu multiobjectivecollectivedecisionoptimizationalgorithmforeconomicemissiondispatchproblem AT qinghuasu multiobjectivecollectivedecisionoptimizationalgorithmforeconomicemissiondispatchproblem AT zenggangxiong multiobjectivecollectivedecisionoptimizationalgorithmforeconomicemissiondispatchproblem |