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Degeneration Recognizing Clonal Selection Algorithm for Multimodal Optimization
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DOI：10.1109/TCYB.2017.2657797.png)
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摘要
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In this paper, a computing speed improvement for the clonal selection algorithm (CSA) is proposed based on a degeneration recognizing (DR) method. The degeneration recognizing clonal selection algorithm (DR-CSA) is designed for solving complex engineering multimodal optimization problems. On each iteration of CSA, there is a large amount of eliminated solutions which are usually neglected. But these solutions do contain the knowledge of the nonoptimal area. By storing and utilizing these data, the DR-CSA is aimed to identify part of the new population as degenerated and eliminate them before the evaluation operation, so that a number of evaluation times can be avoided. This pre-elimination operation is able to save computing time because the evaluation is the main reason for the time cost in the complex engineering optimization problem. Experiments on both test function and a real-world engineering optimization problem (wet spinning coagulating process) are conducted. The results show that the proposed DR-CSA is as accurate as regular CSA and is effective in reducing a considerable amount of computing time.
Keyword:
Clonal selection algorithm (CSA)
complex engineering optimization (CEO)
degeneration recognizing (DR)
multimodal optimization
wet spinning coagulating process
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10.5
论文数:
1.1W
被引数:
5.0W
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