Differential Evolution With Auto-Enhanced Population Diversity
Differential Evolution With Auto-Enhanced Population Diversity
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DOI：
10.1109/tcyb.2014.2339495
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发表时间：
2015-02
影响因子：
11.8
通讯作者：
Ming Yang;Changhe Li;Z. Cai;Jing Guan
中科院分区：
文献类型：
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作者：
Ming Yang;Changhe Li;Z. Cai;Jing Guan
在差分进化（DE）研究中，有许多参数自适应方法，旨在调整突变因子F和交叉概率CR，但这些方法仍然不能解决种群过早收敛和种群停滞的问题。针对这些问题，本文从种群多样性的维度层面研究了种群的适应性，并提出了一种自动增强种群多样性的机制。AEPD能够通过测量种群在每个维度上的分布来识别种群趋同或停滞的时刻。当在一个维度上确定趋同或停滞时，在该维度上将人口多样化到适当的水平或消除停滞问题。AEPD机制被整合到一个流行的DE算法中，并在一组25个CEC2005基准函数上进行了测试。结果表明，AEPD显著提高了原有算法的性能。此外，AEPD有助于算法降低对种群大小的敏感性，种群大小是许多DE算法普遍认为的问题依赖的参数。与其他几种同类算法相比，具有AEPD的DE算法也具有优越的性能。
In differential evolution (DE) studies, there are many parameter adaptation methods, aiming at tuning the mutation factor F and the crossover probability CR. However, these methods still cannot resolve the issues of population premature convergence and population stagnation. To address these issues, in this paper, we investigate the population adaptation regarding population diversity at the dimensional level and propose a mechanism named auto-enhanced population diversity (AEPD) to automatically enhance population diversity. AEPD is able to identify the moments when a population becomes converging or stagnating by measuring the distribution of the population in each dimension. When convergence or stagnation is identified at a dimension, the population is diversified at that dimension to an appropriate level or to eliminate the stagnation issue. The AEPD mechanism was incorporated into a popular DE algorithm and it was tested on a set of 25 CEC2005 benchmark functions. The results showed that AEPD significantly improved the performance of the original algorithms. In addition, AEPD helped the algorithms become less sensitive to population size, a parameter widely considered problem dependent for many DE algorithms. The DE algorithm with AEPD also has a superior performance in comparison with several other peer algorithms.