A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model
dc.cclicence | N/A | en |
dc.contributor.author | Zou, Juan | en |
dc.contributor.author | Li, Qingya | en |
dc.contributor.author | Yang, Shengxiang | en |
dc.contributor.author | Zheng, Jinhua | en |
dc.contributor.author | Peng, Zhou | en |
dc.contributor.author | Pei, Tingrui | en |
dc.date.acceptance | 2018-03-26 | en |
dc.date.accessioned | 2018-06-07T10:16:00Z | |
dc.date.available | 2018-06-07T10:16:00Z | |
dc.date.issued | 2018-03-28 | |
dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. | en |
dc.description.abstract | Traditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution set efficiently after the environmental change. However, these algorithms neglect the role of the dynamic environment in evolution, leading to the lacking of active guieded search. In this paper, a dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model is proposed (DEE-DMOEA). When the environment has not changed, this algorithm makes use of the evolutionary environment to record the knowledge and information generated in evolution, and in turn, the knowledge and information guide the search. When a change is detected, the algorithm helps the population adapt to the new environment through building a dynamic evolutionary environment model, which enhances the diversity of the population by the guided method, and makes the environment and population evolve simultaneously. In addition, an implementation of the algorithm about the dynamic evolutionary environment model is introduced in this paper. The environment area and the unit area are employed to express the evolutionary environment. Furthermore, the strategies of constraint, facilitation and guidance for the evolution are proposed. Compared with three other state-of-the-art strategies on a series of test problems with linear or nonlinear correlation between design variables, the algorithm has shown its effectiveness for dealing with the dynamic multiobjective problems. | en |
dc.funder | National Natural Science Foundation of China | en |
dc.funder | National Natural Science Foundation of China | en |
dc.identifier.citation | Zou, J., Li, Q., Yang, S., Zheng, J., Peng, Z. and Pei, T. (2018) A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model. Swarm and Evolutionary Computation, 44, pp. 247-259 | en |
dc.identifier.doi | https://doi.org/10.1016/j.swevo.2018.03.010 | |
dc.identifier.issn | 2210-6502 | |
dc.identifier.uri | http://hdl.handle.net/2086/16263 | |
dc.language.iso | en_US | en |
dc.peerreviewed | Yes | en |
dc.projectid | 61502408 | en |
dc.projectid | 61673331 | en |
dc.publisher | Elsevier | en |
dc.researchgroup | Centre for Computational Intelligence | en |
dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
dc.subject | Dynamic multiobjective optmization | en |
dc.subject | Evolutionary algorithms | en |
dc.subject | Evolutionary environment | en |
dc.subject | Dynamic evolutionary environment model | en |
dc.title | A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model | en |
dc.type | Article | en |