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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ke Li Deb, K. Qingfu Zhang |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA (Ke Li; Deb, K.) || Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong, China (Qingfu Zhang) |
| Abstract | Achieving balance between convergence and diversity is a basic issue in evolutionary multiobjective optimization (EMO). In this paper, we propose a hybrid EMO algorithm that assigns different selection principles to two separate and co-evolving archives. Particularly, one archive maintains a repository with a competitive selection pressure towards the Pareto-optimal front (PF), the other preserves a population with a satisfied distribution in the objective space. Furthermore, to exploit guidance information towards the Pareto-optimal set (PS), we develop a restricted mating selection mechanism to select mating parents from each archive for offspring generation. Empirical studies are conducted on a set of benchmark problems with complicated PSs. Experimental results demonstrate the effectiveness and competitiveness of our proposed algorithm in balancing convergence and diversity. |
| Starting Page | 900 |
| Ending Page | 907 |
| File Size | 670506 |
| Page Count | 8 |
| File Format | |
| e-ISBN | 9781479974924 |
| DOI | 10.1109/CEC.2015.7256986 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-25 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Convergence Sociology Statistics Optimization Approximation algorithms Benchmark testing Measurement |
| Content Type | Text |
| Resource Type | Article |
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