以群中心策略改良人工蜂群演算法

Enhancing Artificial Bee Colony Algorithm with Centroid Strategy

李維平(Wei-Ping Lee)；李元傑(Yuan-Chieh Lee)；謝明勳(Ming-Hsun Hsieh)

人工蜂群演算法 ； 最佳化演算法 ； 演化式計算 ； Artificial Bee Colony Algorithm ； Optimization Algorithm ； Evolutionary Computation

資訊管理學報

21卷1期（2014 / 01 / 01）

25 - 43

繁體中文

人工蜂群演算法（Artificial Bee Colony）是學者Karaboga於2005年所提出之最佳化演算法，具有良好的穩定性、優秀的求解能力、控制參數少、計算簡潔及易於實現等優點，但也存在後期過早收斂、開發精度不佳等問題。因此，本研究提出一種新式的群中心改良策略，以改善人工蜂群演算法之搜尋能力。本研究以常見的六個測試函數進行實驗，從結果得知，本研究提出之群中心策略有效地加強人工蜂群演算法的搜尋能力，使其在演算法後期持續開發而不會過早收斂，在大部分測試函數上都有明顯的改善。

Artificial Bee Colony algorithm (ABC) is an optimization algorithm proposed by Karaboga in 2005. This method has a good investigation capability, and it is also simple and easy to implement. Though ABC has many advantages, there are still some drawbacks, such as premature convergence and falling into local optimal solutions. In this study, we utilize the centroid strategy to enhance ABC for improving these weak points.In this research we use 6 benchmark functions to test our method and related researches. The results show that our algorithm can enhance the searching capability of ABC and it is better than the other researches in most of benchmark functions.

1. Akay, B.,Karaboga, D.(2012).A modified artificial bee colony algorithm for real-parameter optimization.Information Sciences,192,120-142.
2. Alatas, B.(2010).Chaotic bee colony algorithms for global numerical optimization.Expert Systems with Applications,37(8),5682-5687.
3. Basturk, B.,Karaboga, D.(2006).An Artificial Bee Colony (ABC) algorithm for numeric function optimization.Proceedings of IEEE Swarm Intelligence Symposium (SIS 2006),Indiana, USA:
4. Feng, Q.X.、Ding, H.J.(2008)。,未出版
5. Karaboga, D.(2005).Technical Report-TR06Technical Report-TR06,Erciyes:Erciyes Univ. Press.
6. Karaboga, D.,Akay, B.(2008).Solving large scale numerical problems using artificial bee colony algorithm.Proceedings of the Sixth International Symposium on Intelligent and Manufacturing Systems Features, Strategies and Innovation,Sakarya, Turkiye:
7. Karaboga, D.,Basturk, B.(2007).A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm.Journal of Global Optimization,39(3),459-471.
8. Karaboga, D.,Basturk, B.(2008).On the performance of artificial bee colony (ABC) algorithm.Applied Soft Computing,8(1),687-697.
9. Li, F.L.、Ding, H.J.、Fang, X.(2007)。,未出版
10. Liu, Y.,Qin, Z.,Shi, Z.,Lu, J.(2007).Center particle swarm optimization.Neurocomputing,70(4-6),672-679.
11. Narasimhan, H.(2009).Parallel Artificial Bee Colony (PABC) algorithm.World Congress on Nature & Biologically Inspired Computing (NABIC 2009),Coimbatore, India:
12. Quan, H.,Shi, X.(2008).On the analysis of performance of the improved artificial-bee-colony algorithm.Proceedings of Fourth IEEE International Conference on Natural Computation,Jinan, China:
13. Rahnamayan, S.,Tizhoosh, H.R.,Salama, M.M.A.(2008).Opposition versus randomness in soft computing techniques.Applied Soft Computing Journal,8(2),906-918.
14. Rahnamayan, S.,Tizhoosh, H.R.,Salama, M.M.A.(2006).Opposition-based differential evolution algorithms.Proceedings of IEEE Congress on Evolutionary Computation (CEC 2006),Vancouver, Canada:
15. Tizhoosh, H.R.(2005).Opposition-based learning: a new scheme for machine intelligence.Proceedings of the Computational Intelligence for Modeling Control and Automation (CIMCA.2005),Vienna, Austria:
16. Tsai, P.W.,Pan, J.S.,Liao, B.Y.,Chu, S.C.(2009).Enhanced Artificial Bee Colony Optimization.International Journal of Innovative Computing, Information and Control,5(12),5081-5092.
17. Xu, L.,Krzyzak, A.,Oja, E.(1993).Rival penalized competitive learning for clustering analysis, RBF net, and curve detection.IEEE Transactions on Neural Networks,4(4),636-649.
18. Yi, B.,Qiao, H.Q.,Yang, F.,Xu, C.W.(2010).An improved initialization center algorithm for K-means clustering.Proceedings of 2010 International Conference on Computational Intelligence and Software Engineering (CISE 2010),Wuhan, China:
19. Zhang, C.,Ni, Z.W.,Wu, Z.J.,Gu, L.C.(2009).A novel swarm model with quasi-oppositional particle.Proceedings of International Forum on Information Technology and Applications (IFITA 2009),Chengdu, China:
20. Zhu, G.,Kwong, S.(2010).Gbest-guided artificial bee colony algorithm for numerical function optimization.Applied Mathematics and Computation,217(7),3166-3173.
21. 暴勵、曾建潮(2010)。自我調整搜索空間的混沌蜂群演算法。計算機應用研究，27(4)，1330-1334。
22. 鄭富升(2005)。蟑螂演算法在含限制條件問題的應用。第十屆人工智慧與應用研討會論文集，高雄，台灣:
23. 羅鈞、樊鵬程(2009)。基於遺傳交叉因子的改進蜂群優化算法。計算機應用研究，26(10)，3716-3717。