Artificial bee colony algorithm with an adaptive greedy position update strategy
Artificial bee colony algorithm with an adaptive greedy position update strategy
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DOI：
10.1007/s00500-016-2334-4
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发表时间：
2016-09
期刊：
影响因子：
4.1
通讯作者：
Wei-jie Yu;Zhi-hui Zhan;Jun Zhang
中科院分区：
文献类型：
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作者：
Wei-jie Yu;Zhi-hui Zhan;Jun Zhang
人工蜂群（Artificial bee colony， ABC）是一种新兴的群体智能算法。为了提高开发能力，已经开发了一些贪心ABC变体，但贪心变体通常可靠性较差，可能导致过早收敛，特别是没有对贪心程度进行适当的控制。本文提出了一种自适应ABC算法（AABC），该算法具有新颖的贪婪位置更新策略和贪婪度调整的自适应控制方案。贪心位置更新策略将最优解的信息融入到围观蜜蜂的搜索过程中。这种贪婪策略有利于快速收敛性能。为了适应不同的优化场景，本文提出的自适应控制方案进一步调整算法每次迭代中可供选择的顶层解的大小。调整的依据是考虑蜜蜂当前的搜索趋势。通过将贪心位置更新过程与自适应控制方案相结合，可以同时提高算法的收敛性能和鲁棒性。利用一组基准函数对提出的AABC算法进行了测试。实验结果表明，AABC的组成部分可以显著提高经典ABC算法的性能。此外，AABC比一些现有的ABC变体以及其他最先进的进化算法表现得更好，或者至少与之相比。
Artificial bee colony (ABC) is a recent swarm intelligence algorithm. There have been some greedy ABC variants developed to enhance the exploitation capability, but greedy variants are usually less reliable and may cause premature convergence, especially without proper control on the greediness degree. In this paper, we propose an adaptive ABC algorithm (AABC), which is characterized by a novel greedy position update strategy and an adaptive control scheme for adjusting the greediness degree. The greedy position update strategy incorporates the information of toptsolutions into the search process of the onlooker bees. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the greediness degree to fit for different optimization scenarios, the proposed adaptive control scheme further adjusts the size of top solutions for selection in each iteration of the algorithm. The adjustment is based on considering the current search tendency of the bees. This way, by combining the greedy position update process and the adaptive control scheme, the convergence performance and the robustness of the algorithm can be improved at the same time. A set of benchmark functions is used to test the proposed AABC algorithm. Experimental results show that the components of AABC can significantly improve the performance of the classic ABC algorithm. Moreover, the AABC performs better than, or at least comparably to, some existing ABC variants as well as other state-of-the-art evolutionary algorithms.