PDF(Pubmed)
Abstract：
In this paper, a novel study on the way inter-individual information interacts in meta-heuristic algorithms (MHAs) is carried out using a scheme known as population interaction networks (PIN). Specifically, three representative MHAs, including the differential evolutionary algorithm (DE), the particle swarm optimization algorithm (PSO), the gravitational search algorithm (GSA), and four classical variations of the gravitational search algorithm, are analyzed in terms of inter-individual information interactions and the differences in the performance of each of the algorithms on IEEE Congress on Evolutionary Computation 2017 benchmark functions. The cumulative distribution function (CDF) of the node degree obtained by the algorithm on the benchmark function is fitted to the seven distribution models by using PIN. The results show that among the seven compared algorithms, the more powerful DE is more skewed towards the Poisson distribution, and the weaker PSO, GSA, and GSA variants are more skewed towards the Logistic distribution. The more deviation from Logistic distribution GSA variants conform, the stronger their performance. From the point of view of the CDF, deviating from the Logistic distribution facilitates the improvement of the GSA. Our findings suggest that the population interaction network is a powerful tool for characterizing and comparing the performance of different MHAs in a more comprehensive and meaningful way.
摘要：
在本文中,使用称为种群交互网络（PIN）的方案，对元启发式算法（MHA）中个体间信息交互方式进行了一项新颖的研究。具体来说,三个有代表性的MHA，包括差分进化算法(DE)，粒子群优化算法(PSO)，引力搜索算法(GSA)，以及引力搜索算法的四个经典变体，根据个体间的信息交互以及IEEE大会2017年进化计算基准函数中每个算法的性能差异进行了分析。利用PIN将该算法在基准函数上得到的节点度的累积分布函数(CDF)拟合到7个分布模型中。结果表明,在7种比较算法中,越强的DE越偏向泊松分布，和较弱的PSO，GSA,和GSA变体更偏向Logistic分布。GSA变体与Logistic分布的偏差越大，他们的表现越强。从CDF的角度来看，偏离Logistic分布有利于GSA的改进。我们的发现表明，人口互动网络是以更全面和有意义的方式表征和比较不同MHA表现的强大工具。
