Enhancing the modified artificial bee colony algorithm with neighborhood search
Enhancing the modified artificial bee colony algorithm with neighborhood search
复制标题
通过邻域搜索增强改进的人工蜂群算法
DOI：
10.1007/s00500-015-1977-x
复制
发表时间：
2017-05-01
期刊：
影响因子：
4.1
通讯作者：
Wan, Jianyi
中科院分区：
文献类型：
--
作者：
Zhou, Xinyu;Wang, Hui;Wan, Jianyi
人工蜂群算法作为一种相对较新的优化技术，近年来因其良好的性能而备受关注。然而，它的性能在解决复杂优化问题时也受到了挑战。这种不足主要是由于其解搜索方程勘探效果好，开采效果差。受邻域搜索概念的启发，本文将全局邻域搜索算子引入到ABC中，以平衡其探索和利用能力。在22个基准函数上进行了大量的实验，并在比较研究中包括六种不同的算法，包括四种ABC变体和两种相关的进化算法。比较结果表明，在大多数情况下，我们的方法是能够提供更好的性能方面的解决方案的精度和收敛速度。
As a relatively new optimization technique, in recent years, artificial bee colony (ABC) algorithm has attracted much attention for its good performance. However, its performance has also been challenged in solving complex optimization problems. This insufficiency is mainly caused by its solution search equation, which does well in exploration but badly in exploitation. Inspired by the concept of neighborhood search, in this paper, we introduce a global neighborhood search operator into ABC for balancing its explorative and exploitative capabilities. Extensive experiments are conducted on 22 benchmark functions, and six different algorithms are included in the comparison studies, including four ABC variants and two related evolutionary algorithms. The compared results demonstrate that in most cases our approach is able to provide better performance in terms of solution accuracy and convergence speed.