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Handling Constrained Multi-Objective optimization with Objective Space Mapping to Decision Space Based on Extreme Learning Machine
Zhang H(张浩)1,2,3; Ku T(库涛)1,2,3; Ma LB(马连博)4; Yong, Yibo4
Department数字工厂研究室
Conference Name2020 IEEE Congress on Evolutionary Computation, CEC 2020
Conference DateJuly 19-24, 2020
Conference PlaceVirtual, Glasgow, United kingdom
Author of SourceIEEE Computational Intelligence Society
Source Publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
PublisherIEEE
Publication PlaceNew York
2020
Indexed ByEI
EI Accession number20204109317103
Contribution Rank1
ISBN978-1-7281-6929-3
Keywordconstrained multi-objective optimization extreme learning machine artificial bee colony decomposition nondomination
AbstractConstrained multi-objective optimization is frequently encountered from the point of view of practical problem solving. The difficulty of constrained multi-objective optimization is how to offer guarantee of finding feasible optimal solutions within a specified number of iterations. To address the issue, this paper proposes an innovative optimization framework with objective space mapping to decision space for constrained multiobjective optimization and a novel multi-objective optimization algorithms are proposed based on this framework. Extreme learning machine implements prediction of decision variables from modified objective values with distance measure and adaptive penalty. This algorithm employs the framework of artificial bee colony to divide this optimization process into two phases: the employed bees and the onlooker bees. In the phase of employed bees, multi-objective strategy employs fast non-dominant sort and crowded distance to push the population toward Pareto front. In the phase of onlooker bees, multi-objective strategy employs Tchebycheff approach to enhance the population diversity. The experimental results on a series of benchmark problems suggest that our proposed algorithm is quite effective, in comparison to other state-of-the-art constrained multi-objective optimizers.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27715
Collection数字工厂研究室
Corresponding AuthorMa LB(马连博)
Affiliation1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences
2.Shenyang Institute of Automation, Chinese Academy of Sciences
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences
4.Software College, Northeastern University
Recommended Citation
GB/T 7714
Zhang H,Ku T,Ma LB,et al. Handling Constrained Multi-Objective optimization with Objective Space Mapping to Decision Space Based on Extreme Learning Machine[C]//IEEE Computational Intelligence Society. New York:IEEE,2020.
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