Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems

Mustafa Servet Kiran, Ahmet Babalik

Computer Engineering Department, Sel?uk University, Konya, Turkey.

DOI: 10.4236/jcc.2014.24015                

Abstract

The artificial bee colony (ABC) algorithm is a swarm-based metaheuristic optimization technique, developed by inspiring foraging and dance behaviors of honey bee colonies. ABC consists of four phases named as initialization, employed bee, onlooker bee and scout bee. The employed bees try to improve their solution in employed bees phase. If an employed bee cannot improve self-solution in a certain time, it becomes a scout bee. This alteration is done in the scout bee phase. The onlooker bee phase is placed where information sharing is done. Although a candidate solution improved by onlookers is chosen among the employed bee population according to fitness values of the employed bees, neighbor of candidate solution is randomly selected. In this paper, we propose a selection mechanism for neighborhood of the candidate solutions in the onlooker bee phase. The proposed selection mechanism was based on information shared by the employed bees. Average fitness value obtained by the employed bees is calculated and those better than the aver- age fitness value are written to memory board. Therefore, the onlooker bees select a neighbor from the memory board. In this paper, the proposed ABC-based method called as iABC were applied to both five numerical benchmark functions and an estimation of energy demand problem. Obtained results for the problems show that iABC is better than the basic ABC in terms of solution quality.

Keywords

Artificial Bee Colony; Selection Mechanism; Memory Board; Numerical Optimization; Energy Estimation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Dorigo, M. and Stützle, T. (2004) Ant Colony Optimization. MIT Press, Cambridge.
[2]	Eberhart, R.C. and Kennedy, J. (1995) A New Optimizer Using Particle Swarm Theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, 39-43. http://dx.doi.org/10.1109/MHS.1995.494215
[3]	Karaboga, D. (2005) An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report-TR06, Department of Computer Engineering, Engineering Faculty, Erciyes University.
[4]	Karaboga, D. and Basturk, B. (2008) On the Performance of Artificial Bee Colony (ABC) Algorithm. Applied Soft Computing, 8, 687-697. http://dx.doi.org/10.1016/j.asoc.2007.05.007
[5]	Karaboga, D. and Akay, B. (2009) A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation, 214, 208-132. http://dx.doi.org/10.1016/j.amc.2009.03.090
[6]	Akay, B. (2009) Performance Analysis of Artificial Bee Colony Algorithm on Numerical Optimization Problems. Ph.D. Thesis, Erciyes University, Graduate School of Natural and Applied Sciences, Kayseri.
[7]	Karaboga, N. (2009) A New Design Method Based on Artificial Bee Colony Algorithm for Digital IIR Filters. Journal of the Franklin Institute, 346, 328-348. http://dx.doi.org/10.1016/j.jfranklin.2008.11.003
[8]	Singh, A. (2009) An Artificial Bee Colony Algorithm for the Leaf-Constrained Minimum Spanning Tree Problem. Applied Soft Computing, 9, 625-631. http://dx.doi.org/10.1016/j.asoc.2008.09.001
[9]	Rao, R.S., Narasimham, S. and Ramalingaraju, M. (2008) Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm. International Journal of Electrical Power and Energy Systems Engineering, 1, 116-122.
[10]	Sundar, S. and Singh, A. (2010) A Swarm Intelligence Approach to the Quadratic Minimum Spanning Tree Problem. Information Sciences, 180, 3182-3191.
[11]	Akay, B. and Karaboga, D. (2012) A Modified Artificial Bee Colony Algorithm for Real-Parameter Optimization. Information Science, 192, 120-142. http://dx.doi.org/10.1016/j.ins.2010.07.015
[12]	Karaboga, D. and Akay, B. (2011) A Modified Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Problems. Applied Soft Computing, 11, 3021-3031. http://dx.doi.org/10.1016/j.asoc.2010.12.001
[13]	Pan, Q.-K., Tasgetiren, M.F., Suganthan, P.N. and Chua, T.J. (2011) A Discrete Arti?cial Bee Colony Algorithm for the Lot-Streaming Flow Shop Scheduling Problem. Information Sciences, 181, 2455-2468. http://dx.doi.org/10.1016/j.ins.2009.12.025
[14]	Yeh, W.C. and Hsieh, T.J. (2011) Solving Reliability Redundancy Allocation Problems Using an Artificial Bee Colony Algorithm. Computers & Operations Research, 38, 1465-1473. http://dx.doi.org/10.1016/j.cor.2010.10.028
[15]	Karaboga, D. and Ozturk, C. (2009) Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification. Neural Network World, 19, 279-292.
[16]	Jeya Mala, D. and Mohan, V. (2009) ABC Tester—Artificial Bee Colony Based Software Test Suite Optimization Approach. International Journal of Software Engineering, 15-43.
[17]	Zhu, G. and Kwong, S. (2010) Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization. Applied Mathematics and Computation, 217, 3166-3173. http://dx.doi.org/10.1016/j.amc.2010.08.049
[18]	Alatas, B. (2010) Chaotic Bee Colony Algorithms for Global Numerical Optimization. Expert Systems with Applications, 37, 5682-5687. http://dx.doi.org/10.1016/j.eswa.2010.02.042
[19]	Wong, L.P., Loq, M.Y.H. and Chong, C.S. (2008) A Bee Colony Optimization Algorithm for Travelling Salesman Problem. Second Asia International Conference on Modelling & Simulation, Kuala, Lumpur, 13-15 May 2008, 818- 823.
[20]	TSI (2012) Turkish Statistical Institute. http://www.die.gov.tr
[21]	MENR (2012) The Ministry of Energy and Natural Resources. http://www.enerji.gov.tr
[22]	Kiran, M.S., Özceylan, E., Gündüz, M. and Paksoy, T. (2012) A Novel Hybrid Approach Based on Particle Swarm Optimization and Ant Colony Algorithm to Forecast Energy Demand of Turkey. Energy Conversion and Management, 53, 75-83. http://dx.doi.org/10.1016/j.enconman.2011.08.004
[23]	ünler, A. (2008) Improvement of Energy Demand Forecasts Using Swarm Intelligence: The Case of Turkey with Projections to 2025. Energy Policy, 36, 1937-1944. http://dx.doi.org/10.1016/j.enpol.2008.02.018
[24]	Toksari, M.D. (2007) Ant Colony Optimization Approach to Estimate Energy Demand of Turkey. Energy Policy, 35, 3984-3990. http://dx.doi.org/10.1016/j.enpol.2007.01.028
[25]	Assareh, E., Behrang, M.A., Assari, M.R. and Ghanbarzadeh, A. (2010) Application of PSO and GA Techniques on Demand Estimation of Oil in Iran. Energy, 35, 5223-5229. http://dx.doi.org/10.1016/j.energy.2010.07.043