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A comparative study of artificial bee colony algorithm. (English) Zbl 1169.65053
Summary: The artificial bee colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.
65K05Mathematical programming (numerical methods)
90C15Stochastic programming
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[1]Eiben, A. E.; Smith, J. E.: Introduction to evolutionary computing, (2003)
[2]Eberhart, R. C.; Shi, Y.; Kennedy, J.: Swarm intelligence, (2001)
[3]Holland, J. H.: Adaptation in natural and artificial systems, (1975)
[4]J.R. Koza, Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems, Technical Report STAN-CS-90-1314, Stanford University Computer Science Department, 1990.
[5]I. Rechenberg, in: Cybernetic Solution Path of an Experimental Problem, Library Translation, vol. 1122, Royal Aircraft Establishment, Farnborough, Hants, UK, 1965.
[6]H.P. Schwefel, Kybernetische evolution als strategie der experimentellen forschung in der stromungstechnik, Master’s Thesis, Technical University of Berlin, Germany, 1965.
[7]Fogel, L. J.; Owens, A. J.; Walsh, M. J.: Artificial intelligence through simulated evolution, (1966) · Zbl 0148.40701
[8]R. Storn, K. Price, Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical report, International Computer Science Institute, Berkley, 1995.
[9]Storn, R.; Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization 11, 341-359 (1997) · Zbl 0888.90135 · doi:10.1023/A:1008202821328
[10]Price, K.; Storn, R.; Lampinen, A.: Differential evolution a practical approach to global optimization, (2005)
[11]Bonabeau, E.; Dorigo, M.; Theraulaz, G.: Swarm intelligence: from natural to artificial systems, (1999) · Zbl 1003.68123
[12]L.N. De Castro, F.J. Von Zuben, Artificial immune systems, Part I. Basic theory and applications, Technical Report Rt Dca 01/99, Feec/Unicamp, Brazil, 1999.
[13]J. Kennedy, R.C. Eberhart, in: Particle Swarm Optimization, 1995 IEEE International Conference on Neural Networks, vol. 4, 1995, pp. 1942 – 1948.
[14]Y. Fukuyama, S. Takayama, Y. Nakanishi, H. Yoshida, A particle swarm optimization for reactive power and voltage control in electric power systems, in: Genetic and Evolutionary Computation Conference, 1999, pp. 1523 – 1528.
[15]V. Tereshko, Reaction – diffusion model of a honeybee colony’s foraging behaviour, in: M. Schoenauer (Ed.), Parallel Problem Solving from Nature VI, Lecture Notes in Computer Science, vol. 1917, Springer – Verlag, Berlin, 2000, pp. 807 – 816.
[16]Tereshko, V.; Lee, T.: How information mapping patterns determine foraging behaviour of a honeybee colony, Open systems and information dynamics 9, 181-193 (2002) · Zbl 1016.37049 · doi:10.1023/A:1015652810815
[17]Tereshko, V.; Loengarov, A.: Collective decision-making in honeybee foraging dynamics, Computing and information systems journal 9, No. 3 (2005)
[18]Teodorović, D.: Transport modeling by multi-agent systems: a swarm intelligence approach, Transportation planning and technology 26, No. 4 (2003)
[19]P. Lucic, D. Teodorović, Transportation modeling: an artificial life approach, in: ICTAI, 2002, pp. 216 – 223.
[20]D. Teodorović, M. Dell’Orco, Bee colony optimization – a cooperative learning approach to complex transportation problems, in: Poznan, 3 – 16 September 2005, 10th EWGT Meeting.
[21]H. Drias, S. Sadeg, S. Yahi, 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.
[22]K. Benatchba, L. Admane, M. Koudil, Using bees to solve a data-mining problem expressed as a max-sat one, artificial intelligence and knowledge engineering applications: a bioinspired approach. in: First International Work-Conference on the Interplay Between Natural and Artificial Computation IWINAC 2005, Palmas, Canary Islands, Spain, June 15 – 18 2005.
[23]H.F. Wedde, M. Farooq, Y. Zhang, Beehive: an efficient fault-tolerant routing algorithm inspired by honeybee behavior, ant colony, optimization and swarm intelligence, in: 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5 – 8 2004.
[24]X.S. Yang, Engineering optimizations via nature-inspired virtual bee algorithms. in: Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, Lecture Notes in Computer Science, vol. 3562, Springer, Berlin/Heidelberg, 2005, pp. 317 – 323.
[25]D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi, The bees algorithm, Technical Report, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
[26]D. Karaboga, An idea based on honeybee swarm for numerical optimization, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[27]B. Basturk, D. Karaboga, An artificial bee colony (abc) algorithm for numeric function optimization, in: IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA, May 2006.
[28]Karaboga, D.; Basturk, B.; Powerful, A.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm, Journal of global optimization 39, No. 3, 459-471 (2007) · Zbl 1149.90186 · doi:10.1007/s10898-007-9149-x
[29]Karaboga, D.; Basturk, B.: On the performance of artificial bee colony (abc) algorithm, Applied soft computing 8, No. 1, 687-697 (2008)
[30]D. Karaboga, B. Basturk, in: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, LNCS, vol. 4529/2007, Springer-Verlag, 2007, pp. 789 – 798 (Chapter Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems).
[31]D. Karaboga, B. Basturk Akay, C. Ozturk, in: Modeling Decisions for Artificial Intelligence, LNCS, vol. 4617/2007, Springer-Verlag, 2007, pp. 318 – 329 (Chapter Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks).
[32]D. Karaboga, B. Basturk Akay, An artificial bee colony (abc) algorithm on training artificial neural networks, in: 15th IEEE Signal Processing and Communications Applications, SIU 2007, Eskisehir, Turkiye, June, pp. 1 – 4.
[33]Karaboga, N.: A new design method based on artificial bee colony algorithm for digital iir filters, Journal of the franklin institute 346, No. 4, 328-348 (2009) · Zbl 1166.93351 · doi:10.1016/j.jfranklin.2008.11.003
[34]Singh, Alok: An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem, Applied soft computing 9, No. 2, 625-631 (2009)
[35]Back, T.; Schwefel, H. P.: An overview of evolutionary algorithms for parameter optimization, Evolutionary computation 1, No. 1, 1-23 (1993)
[36]Yao, X.; Liu, Y.: Fast evolution strategies, Control and cybernetics 26, No. 3, 467-496 (1997)
[37]Kohonen, T.: Self-organizing maps, (1995)
[38]Milano, M.; Koumoutsakos, P.; Schmidhuber, J.: Self-organizing nets for optimization, IEEE transactions on neural networks 15, No. 3, 758-765 (2004)
[39]Martinetz, T.; Schulten, S.: A neural-gas network learns topologies, Artificial neural networks, 397-402 (1991)
[40]N. Hansen, A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation, in: IEEE Int. Conf. Evolution. Comput. (ICEC) Proc., 1996, pp. 312 – 317.
[41]A. Hedar, M. Fukushima, Evolution strategies learned with automatic termination criteria, in: Proceedings of SCIS-ISIS 2006, Tokyo, Japan, 2006.
[42]Pham, D. T.; Karaboga, D.: Optimum design of fuzzy logic controllers using genetic algorithms, Journal of systems engineering 1, 114-118 (1991)
[43]Corne, D.; Dorigo, M.; Glover, F.: New ideas in optimization, (1999)
[44]J. Vesterstrom, R. Thomsen, A comparative study of differential evolution particle swarm optimization and evolutionary algorithms on numerical benchmark problems, in: IEEE Congress on Evolutionary Computation (CEC’2004), vol. 3, Piscataway, New Jersey, June 2004, pp. 1980 – 1987.
[45]Boyer, D. O.; Martfnez, C. H.; Pedrajas, N. G.: Crossover operator for evolutionary algorithms based on population features, Journal of artificial intelligence research 24, 1-48 (2005) · Zbl 1123.62053 · doi:http://www.jair.org/contents/v24.html
[46]Junior, A. D.; Silva, R. S.; Mundim, K. C.; Dardenne, L. E.: Performance and parameterization of the algorithm simplified generalized simulated annealing, Genetics and molecular biology 27, No. 4, 616-622 (2004)
[47]Digalakis, J. G.; Margaritis, K. G.: An experimental study of benchmarking functions for genetic algorithms, International journal of computer mathematics 79, No. 4, 403-416 (2002) · Zbl 1147.68881 · doi:10.1080/00207160210939
[48]D. Bratton, J. Kennedy, Defining a standard for particle swarm optimization, in: Swarm Intelligence Symposium, 2007, SIS 2007. IEEE, Honolulu, HI, 2007, pp. 120 – 127.
[49]Michalewicz, Z.; Janikow, C.: Genetic algorithms+Data structures=Evolution programs, (1996)