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dc.contributor.authorZhou, Jianweien
dc.contributor.authorZou, Juanen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorRuan, Ganen
dc.contributor.authorOu, Junweien
dc.contributor.authorZheng, Jinhuaen
dc.date.accessioned2018-11-28T08:51:11Z
dc.date.available2018-11-28T08:51:11Z
dc.date.issued2018-11
dc.identifier.citationZhou, J., Zou, J., Yang, S., Ruan, G., Ou, J. and Zheng, J. (2018) An evolutionary dynamic multi-objective optimization algorithmbased on center-point prediction and sub-population autonomous guidance. 2018 IEEE Symposium Series on Computational Intelligence, Bengaluru, India, November 2018.en
dc.identifier.urihttp://hdl.handle.net/2086/17295
dc.description.abstractDynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems, the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments.en
dc.language.isoen_USen
dc.subjectDynamic multi-objective optimizationen
dc.subjectautonomous guidanceen
dc.titleAn evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidanceen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/ssci.2018.8628655
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Natural Science Foundation of Chinaen
dc.funderNational Natural Science Foundation of Chinaen
dc.projectid61502408en
dc.projectid61673331en
dc.cclicenceN/Aen
dc.date.acceptance2018-09-01en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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