The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

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基于人工蜂群引导的脑电图信号分解大数据优化方法

近年来，数字时代在信息和计算机科学文献中增加了一个新术语，称为大数据。由于新引入术语的个别属性，包括优化问题在内的数据密集型问题的定义已​​发生重大变化，研究现有技术的解决能力，然后开发其专门用于大数据优化的变体已成为重要的研究。话题。人工蜂群 (ABC) 算法受真实蜜蜂聪明觅食特征的启发，是最成功的基于群体智能的元启发式算法之一。在这项研究中，考虑到与大数据相关的优化问题的性质，提出了一种新的基于ABC算法的技术，称为源链接ABC（slinkABC）。slinkABC 算法在 2015 年进化计算大会 (CEC) 大数据优化大赛上提出的大数据优化问题上进行了测试。将实验研究获得的结果与 ABC 算法的不同变体进行比较，包括 gbest-guided ABC (GABC)、ABC/best/1、ABC/best/2、crossover ABC (CABC)、converge-onlookers ABC (COABC) )、快速 ABC (qABC) 和改进的 gbest-guided ABC (MGABC) 算法。除此之外，还将所提出的 ABC 算法的结果与差分进化 (DE) 算法、遗传算法 (GA)、萤火虫算法 (FA) 的结果进行了比较，基于相位的优化 (PBO) 算法和基于粒子群优化 (PSO) 算法的方法。从实验研究中可以看出，与上述优化技术相比，通过考虑大数据优化问题的独特属性修改的 ABC 算法（如在 slinkABC 中）对大多数测试实例产生了更好的解决方案。