Handling Constrained Multi-Objective optimization with Objective Space Mapping to Decision Space Based on Extreme Learning Machine
文献类型：会议论文
| 作者 | Zhang H(张浩)1,2,3 ; Ku T(库涛)1,2,3; Ma LB(马连博)4; Yong YB(雍怡博)4
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| 出版日期 | 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
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| 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 |
| 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.Shenyang Institute of Automation, Chinese Academy of Sciences 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences 4.Software College, Northeastern University |
| 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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