A Self-Adaptive Differential Evolution Algorithm with Dimension Perturb Operator Strategy
碩士 === 中原大學 === 資訊管理研究所 === 98 === Differential Evolution (DE) has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the contr...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/66441978927912746645 |
| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 98 === Differential Evolution (DE) has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In this paper that proposed a dimension perturb strategy and self-adaptive F value in original DE to increase the exploration ability and exploitation ability. Self-adaptive has been found to be highly beneficial for adjusting control parameters. The performance of self-adaptive differential evolution algorithm with dimension perturb operator strategy (PSADE) is showed on the following performance measures by benchmark functions: the solution quality and solution stability. This paper has found that PSADE can efficiently find the global value of these functions.
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