STATISTICALLY GUIDED ARTIFICIAL BEE COLONY ALGORITHM

Year 2017, Volume: 5 Issue: 2, 153 - 169, 01.06.2017

Barış Koçer

https://doi.org/10.15317/Scitech.2017.79

Abstract

Artificial Bee Colony algorithm is one of the naturally inspired meta heuristic method. As

usual, in a meta heuristic method, intuitively appealing way to have better results is extending

calculation time or increasing the fitness evaluation count. But the desired way is acquiring better results

with less computation. So in this work a modified Artificial Bee Colony algorithm which can find better

results with same computation is developed by benefiting statistical observations.

Keywords

Swarm intelligence, Meta heuristic algorithms, S warm intelligence

İstatistiksel Olarak Yönlendirilen Yapay Arı Kolonisi Algoritması

Abstract

Yapay Arı Koloni algoritması, doğadan ilham alan meta sezgisel yöntemlerinden biridir. Meta

sezgisel yöntemle, daha iyi sonuçlar elde etmek için akla ilk gelen çözüm hesaplama süresini arttırmak

veya uygunluk hesaplama sayısını arttırmaktır. Ancak istenilen yol, daha az hesaplama ile daha iyi

sonuçlar elde etmektir. Bu çalışmada, istatistiksel gözlemlerden yararlanarak, aynı uygunluk hesaplama

sayısı ile daha iyi sonuçlar bulunabilen Yapay Arı Koloni Algoritması, geliştirilmiştir.

Keywords

Sürü zekası, Meta sezgisel algoritmalar, S ürü zekası

References

• Akay, B., Karaboga, D., 2012 “A Modified Artificial Bee Colony Algorithm for Real-Parameter Optimization”, Information Sciences, Vol. 192, pp. 120-142. doi:10.1016/j.ins.2010.07.015
• Basturk, B., Karaboga, D., “An Artificial Bee Colony (ABC) Algorithm for Numeric Function Optimization”, IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA, May 2006.
• Dorigo, M., Maniezzo, V., Colorni, A., Positive Feedback as a Search Strategy, Technical Report 91-016, Politecnico di Milano, Italy, 1991.
• Drias, H., Sadeg, S., Yahi, S., “Cooperative Bees Swarm for solving The Maximum Weighted Satisfiability Problem”, Computational Intelligence and Bioinspired Systems. in: 8th International Workshop on Artificial Neural Networks IWANN 2005, Vilanova, Barcelona, Spain, June 8–10 2005.
• Holland, J.H., 1975, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
• Karaboga, D., 2005, An Idea Based on Honeybee Swarm for Numerical Optimization, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.
• Karaboga, D., Basturk, B., 2007, “A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) algorithm”, Journal of Global Optimization, Vol. 39 (3), pp. 459–471.
• Karaboga, D., Akay, B., “Solving Large Scale Numerical Problems Using Artificial Bee Colony Algorithm”, in: Sixth International Symposium on Intelligent and Manufacturing Systems Features, Strategies and Innovation, Sakarya, Türkiye, 14–17 October 2008.
• Karaboga, D., Akay, B., “An Artificial Bee Colony (ABC) Algorithm on Training Artificial Neural Networks”, in: 15th IEEE Signal Processing and Communications Applications, SIU 2007, Eskisehir, Türkiye, pp. 1–4, June 2007.
• Karaboga, D., Basturk, B., 2008, “On The Performance of Artificial Bee Colony (ABC) Algorithm”, Applied Soft Computing, Vol. 8 (1), pp. 687–697.
• Karaboga, D., Akay, B., Ozturk, C., 2007, “Modeling Decisions for Artificial Intelligence, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks”, LNCS 4617/2007, Springer-Verlag, pp. 318–329.
• Karaboga, D., Ozturk, C., Akay, B., “Training Neural Networks with ABC Optimization Algorithm on Medical Pattern Classification”, in: International Conference on Multivariate Statistical Modelling and High Dimensional Data Mining, Kayseri, TURKEY, 19–23 June 2008.
• Kennedy, J., Eberhart, R.C., in: “Particle Swarm Optimization”, 1995 IEEE International Conference on Neural Networks, Vol. 4, pp. 1942–1948, 1995.
• Lucic, P., Teodorovic´, D., “Transportation Modeling: An Artificial Life Approach”, 14th IEEE International Conference on Tools with Artificial Intelligence ( ICTAI, 2002), pp. 216–223, 4-6 November 2002.
• Ozturk, C., Karaboga, D., “Classification by Neural Networks and Clustering with Artificial Bee Colony (ABC) Algorithm”, in: Sixth International Symposium on Intelligent and Manufacturing Systems Features, Strategies and Innovation, Sakarya, Türkiye, 14–17 October 2008.
• Shrivastava A., Gupta M., Swami S., “Enhanced Artificial Bee Colony Algorithm with SPV for Travelling Salesman Problem”, 2015 International Conference on Computing Communication Control and Automation, Pune, pp. 887-891, 2015.
• Teodorovic´, D., 2003, “Transport Modeling by Multi-Agent Systems: a Swarm Intelligence Approach”, Transportation Planning and Technology, Vol. 26 (4), pp. 289-312.
• Yang, X.S., “Engineering Optimizations via Nature-inspired Virtual Bee Algorithms”, in: Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, LNCS, vol. 3562/2005, pp. 317– 323, June 2005.