Nejat YUMUŞAK, Hasan MAKAS

Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms

The artificial bee colony (ABC) algorithm is a metaheuristic search method inspired by bees foraging behaviour. With its global search ability in scout bee phase, it can easily escape from local optimum traps in the problem space. Therefore, it is good at exploration. The migrating birds optimization (MBO) algorithm is another recent metaheuristic search method. It simulates birds V flight formation, which minimizes energy consumption during flight. The MBO algorithm achieves a good convergence to the global optimum by using its own unique benefit mechanism. That is, it has a good exploitation capability. This paper aimed to combine the good exploration property of the ABC algorithm and the good exploitation property of the MBO algorithm via a sequential execution strategy. In the proposed method, firstly, the ABC algorithm runs. This enables solutions to escape from local optimum traps and orientates them to the region in which the global optimum exists. Then the MBO algorithm runs. It performs a good convergence to the global optimum. In the proposed method, some variants of the ABC algorithm and some other well-known optimization algorithms were tested via benchmark functions. It was seen in the experiments that the proposed method gave competitive benchmark test results considering both success rates and convergence performances.

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[1] Boussa¨ıd I, Lepagnot J, Siarry P. A survey on optimization metaheuristics. Informa Sciences 2013; 237: 82-117.
[2] Blum C, Roli A. Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 2003; 35: 268-308.
[3] Yan TS, Tao YQ, Cui DW. Research on handwritten numeral recognition method based on improved genetic algorithm and neural network. In: Proceedings of international conference on wavelet analysis and pattern recognition; 02-04 November 2007; Beijing, China. pp. 1271-1276.
[4] Holland JH. Adaptation in Natural and Artificial Systems. Ann Arbor, Michigan, USA: University of Michigan Press, 1975.
[5] Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science 1983; 220: 671-680.
[6] Glover F. Future paths for integer programming and links to artificial intelligence. Comput Oper Res 1986; 13: 533-549.
[7] Dorigo M. Optimization, learning and natural algorithms. PhD, Politecnico di Milano, Milano, Italy, 1992.
[8] Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science; 46 October 1995; Nagoya, Japan. pp. 39-43.
[9] Storn R, Price K. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 1997; 11: 341-359.
[10] Geem ZW, Kim JH, Loganathan GV. A new heuristic optimization algorithm: harmony search. Simulation 2001; 76: 60-68.
[11] Mucherino A, Seref O. A novel meta-heuristic search for global optimization. In: Proceedings of the Conference on Data Mining, System Analysis and Optimization in Biomedicine; 2830 March 2007; Gainesville, FL, USA. pp. 162-173.
[12] Karabo˘ga D. An idea based on honey bee swarm for numerical optimization. Technical Report TR06. Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[13] Yang XS. Firefly algorithm. In: Yang XS, editor. Nature - Inspired Metaheuristic Algorithms. United Kingdom: Luniver Press, 2010. pp. 81-90.
[14] Shah-Hosseini H. The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. International Journal of Bio-inspired Computation 2009; 1: 71-79.
[15] Yang XS, Deb S. Cuckoo search via l´evy flights. In: Proceeings of World Congress on Nature & Biologically Inspired Computing (NaBIC); 0911 December 2009; Coimbatore, India. pp. 210-214.
[16] Yang XS, Deb S. Engineering optimisation by cuckoo search. Int J Math Modelling & Num Optimisation 2010; 1: 330-343.
[17] Yang XS. A new metaheuristic bat-inspired algorithm. In: Cruz C, Gonz´alez JR, Krasnogor N, Pelta DA, Terrazas G, editors. Nature Inspired Cooperative Strategies for Optimization - NICSO (Studies in Computational Intelligence; 284). Granada, Spain: Springer, 2010. pp. 65-74.
[18] Yang XS. Bat algorithm for multi-objective optimisation. Int J BioInspired Computation 2011; 3: 267-274.
[19] Duman E, Uysal M, Alkaya AF. Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inform Sciences 2012; 217: 65-77.
[20] Pandian SMV, Thanushkodi K. Considering transmission loss for an economic dispatch problem without valve-point loading using an EP-EPSO algorithm. Turk J Elec Eng & Comp Sci 2012; 20: 1259-1267.
[21] Mutluer M, Bilgin O. Application of a hybrid evolutionary technique for efficiency determination of a submersible induction motor. Turk J Elec Eng & Comp Sci 2011; 19: 877-890.
[22] Jaddi NS, Abdullah S. Hybrid of genetic algorithm and great deluge algorithm for rough set attribute reduction. Turk J Elec Eng & Comp Sci 2013; 21: 1737-1750.
[23] Bu TM, Yu SN, Guan HW. Binary-coding-based ant colony optimization and its convergence. J Comput Sci Technol 2004; 19: 472-478.
[24] Hu XM, Zhang J, Li Y. Orthogonal methods based ant colony search for solving continuous optimization problems. J Comput Sci Technol 2008; 23: 2-18.
[25] Chu ZF, Xia YS, Wang LY. Cell mapping for nanohybrid circuit architecture using genetic algorithm. J Comput Sci Technol 2012; 27: 113-120.
[26] Li J, Liu X. Melt index prediction by RBF neural network optimized with an MPSO-SA hybrid algorithm. Neurocomputing 2011; 74: 735-740.
[27] Pop PC, Matei O, Sitar CP. An improved hybrid algorithm for solving the generalized vehicle routing problem. Neurocomputing 2013; 109: 76-83.
[28] Sun Y, Zhang L, Gu X. A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems. Neurocomputing 2012; 98: 76-89.
[29] Yi H, Duan Q, Liao TW. Three improved hybrid metaheuristic algorithms for engineering design optimization. Appl Soft Comp 2013; 13: 2433-2444.
[30] Yaghini M, Karimi M, Rahbar M. A hybrid metaheuristic approach for the capacitated p-median problem. Appl Soft Comp 2013; 13: 3922-3930.
[31] Liefooghe A, Verel S, Hao JK. A hybrid metaheuristic for multi-objective unconstrained binary quadratic programming. Appl Soft Comp 2014; 16: 10-19.
[32] Cavuslu MA, Karakuzu C, Karakaya F. Neural identification of dynamic systems on FPGA with improved PSO learning. Appl Soft Comp 2012; 12: 2707-2718.
[33] Makas H, Yumusak N. New cooperative and modified variants of the migrating birds optimization algorithm. In: Proceedings of the International Conference on Electronics, Computer and Computation (ICECCO); 0709 November 2013; Ankara, Turkey. pp. 176-179.
[34] Fornarelli G, Giaquinto A. An unsupervised multi-swarm clustering technique for image segmentation. Swarm and Evolutionary Computation 2013; 11: 31-45.
[35] Wang Y, Wang H, Lei X, Jiang Y, Song X. Flood simulation using parallel genetic algorithm integrated wavelet neural networks. Neurocomputing 2011; 74: 2734-2744.
[36] Banos R, Ortega J, Gil C, Fern´andez A, Toro F. A simulated annealing-based parallel multiobjective approach to vehicle routing problems with time windows. Expert Syst Appl 2013; 40: 1696-1707.
[37] Yusof R, Khalid M, Hui GT, Yusof SM, Othman MF. Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm. Appl Soft Comp 2011; 11: 5782-5792.
[38] Yu B, Yang Z, Sun X, Yao B, Zeng Q, Jeppesen E. Parallel genetic algorithm in bus route headway optimization. Appl Soft Comp 2011; 11: 5081-5091.
[39] Li G, Niu P, Xiao X. Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl Soft Comp 2012; 12: 320-332.
[40] Zhu G, Kwong S. Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math and Comput 2010; 217: 3166-3173.
[41] Xiang W, An M. An efficient and robust artificial bee colony algorithm for numerical optimization. Computers & Operations Research 2013; 40: 1256-1265.
[42] Babayigit B, Ozdemir R. A modified artificial bee colony algorithm for numerical function optimization. In: Proceedings of IEEE Symposium on Computers and Communications (ISCC); 0104 July 2012; Cappadocia, Turkey. pp. 245-249.
[43] Gao W, Liu S. A modified artificial bee colony algorithm. Computers & Operations Research 2012; 39: 687-697.
[44] Sharma TK, Pant M. Golden search based artificial bee colony algorithm and its application to solve engineering design problems. In: Proceedings of International Conference on Advanced Computing & Communication Technologies (ACCT); 78 Jan. 2012; Rohtak, India. pp. 156-160.
[45] Zhang Y, Zeng P, Wang Y, Zhu B, Kuang F. Linear weighted Gbest-guided artificial bee colony algorithm. In: Proceedings of International Symposium on Computational Intelligence and Design (ISCID); 2829 October 2012; Hangzhou, China. pp. 155-159.
[46] Fister I, Fister I Jr., Zumer V. Memetic artificial bee colony algorithm for large-scale global optimization. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC); 1015 June 2012; Brisbane, Australia. pp. 1-8.
[47] Tsai PW, Pan JS, Liao BY, Chu SC. Enhanced artificial bee colony optimization. Int J Innov Comput I 2009; 5: 5081-5092.
[48] Kang F, Li J, Ma Z. Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inform Sciences 2011; 181: 3508-3531.
[49] Gao W, Liu S. A modified artificial bee colony algorithm. Computers & Operations Research 2012; 39: 687-697.
[50] Banharnsakun A, Achalakul T, Sirinaovakul B. The best-so-far selection in artificial bee colony algorithm. Appl Soft Comp 2011; 11: 2888-2901.
[51] Mustaffa Z, Yusof Y, Kamaruddin SS. Gasoline price forecasting: an application of LSSVM with improved ABC. Procedia - Social and Behavioral Sciences 2014; 129: 601-609.
[52] Karaboga D, Akay B. A comparative study of artificial bee colony algorithm. Appl Math Comput 2009; 214: 108-132.
[53] Akay B. Performance analysis of artificial bee colony algorithm on numerical optimization problems. PhD, Erciyes University, Kayseri, Turkey, 2009.
[54] Lissaman PBS, Schollenberger CA. Formation flight of bird. Science 1970; 168: 1003-1005.
[55] De Jong K. An analysis of the behavior of a class of genetic adaptive systems. PhD, University of Michigan, Michigan, USA, 1975.
[56] Rastrigin LA. Systems of Extremal Control. Moscow, Russia: Nauka, 1974.
[57] Griewank AO. Generalized descent for global optimization. J Optimiz Theory App 1981; 34: 11-39.
[58] Michalewicz Z. Genetic Algorithms C Data Structures D Evolution Programs. New York, NY, USA: Springer, 1992.
[59] Rahnamyan S., Tizhoosh HR, Salama NMM. A novel population initialization method for accelerating evolutionary algorithms. Computers and Mathematics with Applications 2007; 53: 1605-1614.
[60] B¨ack T, Schwefel, HP. An overview of evolutionary algorithms for parameter optimization. Evol Comput 1993; 1: 1-23.
[61] Schwefel HP. Numerical Optimization of Computer Models. Chichester, UK: Wiley, 1981.
[62] Ackley DH. A Connectionist Machine for Genetic Hill Climbing. Norwell, MA, USA: Kluwer Academic Publishers, 1987.