Artificial bee colony algorithm with an adaptive greedy position update strategy
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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 top t solutions 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.
KeywordsAdaptive parameter control Artificial bee colony (ABC) Global optimization Swarm intelligence
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61502544, 61402545, and 61332002).
Compliance with ethical standards
Conflict of interest
Wei-Jie Yu, Zhi-Hui Zhan, and Jun Zhang declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Akay B, Karaboga D (2009) Parameter tuning for the artificial bee colony algorithm. In: Nguyen NT, Kowalczyk R, Chen SM (eds) Computational collective intelligence. Semantic web, social networks and multiagent systems. Springer, Berlin, pp 608–619Google Scholar
- Auger A, Hansen N (2005) Performance evaluation of an advanced local search evolutionary algorithm. In: The 2005 IEEE congress on evolutionary computation, 2005, vol 2, pp 1777–1784Google Scholar
- Banharnsakun A, Achalakul T, Sirinaovakul B (2010) ABC-GSX: a hybrid method for solving the traveling salesman problem. In: 2010 second world congress on nature and biologically inspired computing, pp 7–12Google Scholar
- Bao L, Zeng JC (2009) Comparison and analysis of the selection mechanism in the artificial bee colony algorithm. In: 2009 ninth international conference on hybrid intelligent systems, vol 1, pp 411–416Google Scholar
- Bharti KK, Singh PK (2015) Chaotic gradient artificial bee colony for text clustering. Soft Comput 20(3):1–14Google Scholar
- Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering DepartmentGoogle Scholar
- Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, KanGAL reportGoogle Scholar
- Tinghuai M, Jinjuan Z, Meili T, Yuan T, Abdullah AD, Mznah AR, Sungyoung L (2015) Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inf Syst 98(4):902–910Google Scholar
- Tsai P, Pan J, Liao B, Chu S (2008) Interactive artificial bee colony (IABC) optimization. In: Proceedings of ISI2008 (Taiwan)Google Scholar
- Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102Google Scholar
- Yu WJ, Zhang J, Chen WN (2013) Adaptive artificial bee colony optimization. In: Proceedings of the 15th annual conference on genetic and evolutionary computation, pp 153–158Google Scholar
- Zhou X, Wang H, Wang M, Wan J (2015) Enhancing the modified artificial bee colony algorithm with neighborhood search. Soft Comput. doi: 10.1007/s00500-015-1977-x