Success-history based parameter adaptation for Differential Evolution
Success-history based parameter adaptation for Differential Evolution
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DOI：
10.1109/cec.2013.6557555
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Ryoji Tanabe;A. Fukunaga
中科院分区：
文献类型：
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作者：
Ryoji Tanabe;A. Fukunaga
微分进化是一种简单而有效的数值优化方法。由于DE的搜索效率在很大程度上取决于其控制参数设置，因此最近有很多研究工作在开发DE的自适应机制。我们提出了一种新的DE参数自适应技术，该技术使用成功控制参数设置的历史记忆来指导未来控制参数值的选择。通过对CEC2013基准集中的28个问题、CEC2005基准集和13个经典基准问题集的比较，对所提方法进行了评价。实验结果表明，采用基于成功历史的参数自适应方法的DE与目前最先进的DE算法相比具有竞争力。
Differential Evolution is a simple, but effective approach for numerical optimization. Since the search efficiency of DE depends significantly on its control parameter settings, there has been much recent work on developing self-adaptive mechanisms for DE. We propose a new, parameter adaptation technique for DE which uses a historical memory of successful control parameter settings to guide the selection of future control parameter values. The proposed method is evaluated by comparison on 28 problems from the CEC2013 benchmark set, as well as CEC2005 benchmarks and the set of 13 classical benchmark problems. The experimental results show that a DE using our success-history based parameter adaptation method is competitive with the state-of-the-art DE algorithms.