差分进化算法中参数自适应选择策略研究

Research on Parameter Self-selection Strategy of Differential Evolution

参数选择本身是一个组合优化问题,尽管过去提出了很多方法,但是参数选择依然令人困惑,为此提出适用于差分进化算法的参数自适应选择策略。该策略在进化的过程中动态评估参数的性能,并根据其结果指导下一次迭代过程的参数选择。从参数库的建立、参数评分机制和参数配置机制3方面展开研究,对比实验结果表明,该方法效果良好。

The selection of the parameter itself is a combinatorial optimization problem. Although a considerable number of works have been conducted,it is known to be a puzzled task. In this paper,a DE algorithm was proposed that uses a new mechanism to parameter self-selection, which dynamically learns from their previous experiences and selects the best performing combinations of parameters for the next generation during the convergence process. We firstly designed the mechanism including three aspects：building of parameter database, score of parameter performance and selection of parameter combination, then we conducted the experiments on some benchmark functions to judge the performance. The results show that the DE with the new mechanism obtains promising performance.