应用于竞争差异进化的平行迁移模型

2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Pub Date : 2011-09-26 DOI:10.1109/SYNASC.2011.45

P. Bujok, J. Tvrdík

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引用次数: 3

摘要

研究了并行性对竞争自适应差分进化性能的影响。实验比较了文献中描述的两种连续竞争差异进化变体和16种新的平行进化变体。本研究中所有的并行差分演化变体都是基于具有星型拓扑结构的迁移模型。在两个维度(D = 10和D = 30)的六个基准函数上对算法进行比较。函数评估的次数和搜索的可靠性作为算法性能的基本特征。实验结果表明，将并行性应用于竞争差分进化，适当设置控制并行模型的参数，至少在某些问题上可以显著提高算法的性能，降低计算量。

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Parallel Migration Models Applied to Competitive Differential Evolution

The influence of parallelism on the performance of competitive adaptive differential evolution is studied. Two serial competitive differential evolution variants described in literature and sixteen novel parallel variants were experimentally compared. All the parallel differential evolution variants in this study are based on a migration model with the star topology. The algorithms were compared on six benchmark functions with two levels of dimension (D = 10 and D = 30). The number of the function evaluations and the reliability rate of the search were used as basic characteristics of algorithm's performance. The experimental results show that the parallelism applied to competitive differential evolution together with a proper setting of the parameters controlling the parallel model can improve the performance of the algorithm and decrease the computational costs significantly at least in some problems.

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2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

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