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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hua-Qing Min Yu-Ren Zhou Yan-Sheng Lu Jia-zhi Jiang |
| Copyright Year | 2006 |
| Description | Author affiliation: Coll. of Comput. Sci. & Eng., HuaZhong Univ. of Sci. & Technol., Wuhan (Hua-Qing Min) |
| Abstract | Constrained multi-objective optimization problems (CMOP) are challenging and difficult to solve. In this paper, a simple and practical evolutionary algorithm for constrained multi-objective optimization problems (EACMOP) is presented, by defining constraints using non-parameter punitive functions, using Pareto strength value to represent Pareto order strength among individuals and using crowding density to ensure group diversity. It defines the evolutionary algorithm fitness functions by combining constraint treatment, comparison of Pareto strength optimization and crowding density. Test results on several benchmark functions showed that the approach is effective and robust |
| Sponsorship | IEEE Comput. Soc. South China Univ. of Technol |
| Starting Page | 667 |
| Ending Page | 670 |
| File Size | 224550 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769527515 |
| DOI | 10.1109/APSCC.2006.30 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-12 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Evolutionary computation Pareto optimization Educational institutions Evolutionary algorithms constrained Design optimization Constraint optimization Computer science Diversity reception Benchmark testing Robustness multi-objective optimization |
| Content Type | Text |
| Resource Type | Article |
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