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An Adaptive Cauchy Differential Evolution Algorithm with Bias Strategy Adaptation Mechanism for Global Numerical Optimization

机译:具有偏置策略适应机制的自适应Cauchy差分演化算法,用于全局数值优化

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—Appropriately adapting mutation strategies is a challengeable problem of the literature of the Differential Evolution (DE). The Strategy adaptation Mechanism (SaM) can convert a control parameter adaptation algorithm to a strategy adaptation algorithm. To improve the quality of optimization result, the exploration property is important in the early stage of optimization process and the exploitation property is significant in the late stage of optimization process. To ensure these, we modified the SaM for strictly controlling a balance between the exploration and the exploitation properties, which called the bias SaM (bSaM). We extended the Adaptive Cauchy Differential Evolution (ACDE) by attaching the bSaM. We compared the bSaM with SaM and the bSaM extended ACDE with the state-ofthe- art DE algorithms in various benchmark problems. The result of the performance evaluation showed that the bSaM and the bSaM extended ACDE performs better than SaM and the state-of-the-art DE algorithms not only unimodal but also multimodal benchmark problems.
机译:- 适当地调整突变策略是差分进化的文献的有挑战性问题(DE)。策略适应机制(SAM)可以将控制参数适应算法转换为策略适应算法。为了提高优化结果的质量,勘探属性在优化过程的早期阶段很重要,剥削性能在优化过程的后期显着。为确保这些,我们修改了SAM,以严格控制探索和开发属性之间的平衡,称为偏见SAM(BSAM)。我们通过附加BSAM扩展了自适应Cauchy差分进化(ACDE)。我们将BSAM与SAM和BSAM扩展的BSAM与各种基准问题中的最先进的DE算法进行了比较。性能评估的结果表明,BSAM和BSAM扩展ACDE的表现优于SAM和最先进的DE算法,不仅是单向的,而且还具有多模式基准问题。

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