Analysis of Population Size in Artificial Bee Colony Algorithm
Analysis of Population Size in Artificial Bee Colony Algorithm
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DOI：
10.1109/smc.2018.00615
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Xianneng Li;Meihua Yang;Huiyan Yang;Shizhe Wu;Guangfei Yang;Min Han;S. Kanae
中科院分区：
文献类型：
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作者：
Xianneng Li;Meihua Yang;Huiyan Yang;Shizhe Wu;Guangfei Yang;Min Han;S. Kanae
人工蜂群（ABC）算法在求解连续全局优化问题（CGOP）中引起了越来越多的关注，已有许多算法扩展。然而，现有的研究通常采用相同的群体大小来比较不同的ABC变体，而不管通常合适的群体大小应该是算法依赖的。在这里，我们重点分析人口规模。这项研究是在一组基准CGOP下在几个众所周知的ABC变体中进行的。我们证明了：i）在独立最优的种群规模下，标准ABC可以与其高级变体相比具有竞争力，ii）最有利可图的种群规模与算法的开发/探索能力有关。我们预计，这项研究将提供有用的见解，以指导ABC的适当使用，以及其进一步的增强。
Artificial bee colony (ABC) algorithm has attracted growing interest for the continuous global optimization problems (CGOPs), where numerous algorithmic extensions have been developed. However, existing studies generally employ identical population size to perform the comparison among different ABC variants, regardless a fact that the generally suitable population size should be algorithm-dependent. Here we focus on the analysis of population size. This study is conducted in several well-known ABC variants under a set of benchmark CGOPs. We demonstrate that i) with the independently optimal population size, standard ABC can perform competitively comparing with its advanced variants, and ii) the most remunerative population size is related to the algorithmic exploitation/exploration ability. We anticipate that this study will provide useful insights to guide the appropriate usage of ABC, as well as its further enhancements.