The big data term and its formal definition have changed the properties of some of the computational problems. One of the problems for which the fundamental properties change with the existence of the big data is the optimization problems. Artificial bee colony (ABC) algorithm inspired by the intelligent source search, consumption and communication characteristics of the real honey bees has proven its efficiency on solving different numerical and combinatorial optimization problems. In this study, the standard ABC algorithm and its well-known variants including the gbest-guided ABC algorithm, the differential evolution based ABC/best/1 and ABC/best/2 algorithms, crossover ABC algorithm, converge-onlookers ABC algorithm and quick ABC algorithm were assessed using the electroencephalographic signal decomposition based optimization problems introduced at the 2015 Congress on Evolutionary Computing Big Data Competition. The experimental studies on solving big data optimization problems showed that the phase-divided structure of the standard ABC algorithm still protects its advantageous sides when the candidate food sources or solutions are generated by referencing the global best solution in the onlooker bee phase.

中文翻译：

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人工蜂群算法及其变体在大数据优化中的比较研究

大数据术语及其正式定义已经改变了一些计算问题的性质。随着大数据的存在，基本属性发生变化的问题之一是优化问题。人工蜂群（ABC）算法的灵感来自于真正的蜜蜂的智能源搜索，消耗和通信特性，已证明其解决各种数值和组合优化问题的效率。在这项研究中，标准ABC算法及其众所周知的变体包括gbest指导的ABC算法，基于差分演化的ABC / best / 1和ABC / best / 2算法，交叉ABC算法，使用2015年进化计算大数据竞赛大会上介绍的基于脑电信号分解的优化问题评估了会聚围观ABC算法和快速ABC算法。解决大数据优化问题的实验研究表明，当通过参考围观蜂阶段的全球最佳解决方案生成候选食物来源或解决方案时，标准ABC算法的分相结构仍可保护其有利方面。