差分进化中的精英强化策略

Chun-Ling Lin, Sheng-Ta Hsieh
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引用次数: 0

摘要

差分进化(DE)是一种强大的优化器。它有各种各样的突变策略来解决广泛的应用。为了吸取不同突变策略的优点,弥补其缺点,提出了精英强化策略。因此,在所提出的方法中,DE将涉及多个向量组,每个向量组的突变策略应该不同。如果某一向量群无法找到较好的解,则采用精英强化策略,加入少数精英向量来替换性能较差的向量。精英向量可以引导其他向量走向潜在的解空间。为了验证所提出的方法,采用CEC 2005测试函数进行了实验。此外,还涉及三种DE变体进行比较。结果表明,该方法的性能优于其他三种DE方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Elitist Reinforcement Strategy for Differential Evolution
Differential evolution (DE) is one of powerful optimizers. It has various mutation strategies for solving widely applications. In order to draw on the strong points of different mutation strategy and to offset their weaknesses, the elitist reinforcement strategy is proposed. Thus, in the proposed method, there are more than one vector-group will be involved in DE, the mutation strategy of each vector-group should be different. If one of vector-group cannot find better solution, elitist reinforcement strategy will join a few elitist vectors to replace vectors which with poor performance. The elitist vectors can guide other vectors toward to potential solution space. In order to test proposed method, the CEC 2005 test functions are adopted for experiments. Also, three DE variants are involved for comparison. From the results, it shows that the proposed method performs better than other three DE methods.
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