Handling Constrained Multi-Objective optimization with Objective Space Mapping to Decision Space Based on Extreme Learning Machine

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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)1,3,4; Ku T(库涛)1,3,4; Ma LB(马连博)2; Yong YB(雍怡博)2
	2020
会议日期	July 19-24, 2020
会议地点	Virtual, Glasgow, United kingdom
关键词	constrained multi-objective optimization extreme learning machine artificial bee colony decomposition nondomination
页码	1-7
英文摘要	Constrained 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.
源文献作者	IEEE Computational Intelligence Society
产权排序	1
会议录	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-7281-6929-3
WOS记录号	WOS:000703998200091
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/27715]
专题	沈阳自动化研究所_数字工厂研究室
通讯作者	Ma LB(马连博)
作者单位	1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences 2.Software College, Northeastern University 3.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences 4.Shenyang Institute of Automation, Chinese Academy of Sciences
推荐引用方式 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]. 见:. Virtual, Glasgow, United kingdom. July 19-24, 2020.