Analysis of Population Size in Artificial Bee Colony Algorithm

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.