A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model
Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The mai...
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| Vydáno v: | Soft computing (Berlin, Germany) Ročník 18; číslo 10; s. 1913 - 1929 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2014
Springer Nature B.V |
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments. |
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| AbstractList | Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments. |
| Author | Chen, Yangyang Liu, Ruochen Ma, Wenping Jiao, Licheng Mu, Caihong |
| Author_xml | – sequence: 1 givenname: Ruochen surname: Liu fullname: Liu, Ruochen email: aliang3399@gmail.com organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University – sequence: 2 givenname: Yangyang surname: Chen fullname: Chen, Yangyang organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University – sequence: 3 givenname: Wenping surname: Ma fullname: Ma, Wenping organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University – sequence: 4 givenname: Caihong surname: Mu fullname: Mu, Caihong organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University – sequence: 5 givenname: Licheng surname: Jiao fullname: Jiao, Licheng organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University |
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| References_xml | – reference: FarinaMAmatoPDebKDynamic multi-objective optimization problems: test cases, approximations and applicationsIEEE Trans Evol Comput20048542544210.1109/TEVC.2004.831456 – reference: Grefenstette JJ (1992) Genetic algorithms for changing environments. Parallel problem solving from nature, Brussels, pp 137–144 – reference: Deb K, Bhaskara UN, Karthik S (2007) Dynamic multi-Objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: Fourth international conference on evolutionary multi-criterion optimization. LNCS, vol 4403, Matsushima, Springer, pp 803–807 – reference: Morrison RW, Jong KA (2000) Triggered hyper-mutation revisited. 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