Abstract
Classical multi-objective evolutionary algorithms (MOEAs) have been proven to be inefficient for solving multi-objective optimizations problems when the number of objectives increases due to the lack of sufficient selection pressure towards the Pareto front (PF). This poses a great challenge to the design of MOEAs. To cope with this problem, researchers have developed reference-point based methods, where some well-distributed points are produced to assist in maintaining good diversity in the optimization process. However, the convergence speed of the population may be severely affected during the searching procedure. This paper proposes a proportion-based selection scheme (denoted as PSS) to strengthen the convergence to the PF as well as maintain a good diversity of the population. Computational experiments have demonstrated that PSS is significantly better than three peer MOEAs on most test problems in terms of diversity and convergence.
| Original language | English |
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| Title of host publication | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 : Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-7 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781538627259 |
| DOIs | |
| Publication status | Published - 1 Jul 2017 |
| Externally published | Yes |
Publication series
| Name | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings |
|---|---|
| Volume | 2018-January |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Funding
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61502408 and 61673331, the Education Department Major Project of Hunan Province under Grant No. 17A212615, the CERNET Innovation Project under Grant No. NGII20150302, and the Research Project on Teaching Reform of Colleges and Universities in Hunan (Network Construction and Auxiliary Teaching of Computer Culture Foundation).