An individual dependent multi-colony artificial bee colony algorithm
An individual dependent multi-colony artificial bee colony algorithm
复制标题
一种个体依赖的多群体人工蜂群算法
DOI：
10.1016/j.ins.2019.02.014
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发表时间：
2019-06-01
影响因子：
8.1
通讯作者：
Wang, Xuping
中科院分区：
文献类型：
--
作者：
Zhou, Jiajun;Yao, Xifan;Wang, Xuping
人工蜂群（ABC）是一种著名的基于群体智能的算法，它模拟蜜蜂对食物来源的觅食行为。然而，基本ABC只进化出一个群体，雇佣蜂阶段和旁观者阶段都使用相同的解搜索方程，在探索方面表现良好，但在开发方面表现不佳。受分工良好的工作效率和互补元素协调的启发，我们提出了一种新的个体相关的多群体ABC算法，简称IDABC，该算法根据所涉及个体的适应度函数值将整个群体分为三个子群体，即劣子群体、中子群体和优势子群体。三个具有不同搜索偏差的进化算子被引入到相应的子群体中以发挥不同的作用。此外，我们通过结合个体的适应度和距离信息以及根据搜索经验动态调整的食物源扰动涉及的控制参数来改进相关的进化算子。并提出了一种正交学习机制用于侦察蜂搜索，以产生潜在的解决方案。所提出的 IDABC 在 CEC2013 和 CEC2014 竞赛中的一组基准实例中进行了检查，比较结果证明了 IDABC 的竞争性能。 (C) 2019 Elsevier Inc. 保留所有权利。
Artificial bee colony (ABC) is a well-known swarm intelligence based algorithm that simulates the foraging behavior of honey bees for food sources. However, the basic ABC only evolves one colony and both the employed bee phase and onlooker phase utilize the same solution search equation, which performs well in exploration but poorly in exploitation. Inspired by the good working efficiency of labor division and the coordination of complementary elements, we propose a novel individual dependent multi-colony ABC algorithm, abbreviated as IDABC, in which the whole colony is divided into three sub-colonies, i.e., inferior sub-colony, mid sub-colony and superior sub-colony, based on the fitness function values of the individuals involved. Three evolution operators with different searching biases are introduced into the corresponding sub-colonies in order to play different roles. Furthermore, we improve the related evolution operators by incorporating the fitness and distance information of individuals and the control parameters involved in the food source perturbation that are dynamically adjusted according to the search experience. And an orthogonal learning mechanism is suggested for scout bee searching so as to generate potential solutions. The proposed IDABC is examined in a set of benchmark instances taken from the CEC2013 and CEC2014 competition, and comparative results demonstrate the competitive performance of IDABC. (C) 2019 Elsevier Inc. All rights reserved.