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Improved artificial bee colony algorithm for global optimization. (English) Zbl 1260.68457

Summary: The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter \(M\), we propose two improved solution search equations, namely “ABC/best/1” and “ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability \(p\) to control the frequency of introducing “ABC/rand/1” and “ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.

MSC:

68W20 Randomized algorithms
90C26 Nonconvex programming, global optimization
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)

Software:

JADE; ABC
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Full Text: DOI

References:

[1] Tang, K. S.; Man, K. F.; Kwong, S.; He, Q., Genetic algorithms and their applications, IEEE Signal Process. Mag., 13, 22-37 (1996)
[2] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proc. IEEE Congr. Evol. Comput. Australia, 1995, pp. 1942-1948.; J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proc. IEEE Congr. Evol. Comput. Australia, 1995, pp. 1942-1948.
[3] Dorigo, M.; Gambardella, L. M., Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput., 12, 53-66 (1997)
[4] Simon, D., Biogeography-based optimization, IEEE Trans. Evol. Comput., 12, 702-713 (2008)
[5] D. Karaboga, An idea based on honeybee swarm for numerical optimization, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.; D. Karaboga, An idea based on honeybee swarm for numerical optimization, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[6] Karaboga, D.; Basturk, B., A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Global Optim., 39, 459-471 (2007) · Zbl 1149.90186
[7] Karaboga, D.; Basturk, B., On the performance of artificial bee colony (ABC) algorithm, Appl. Soft Comput., 8, 687-697 (2008)
[8] Karaboga, D.; Basturk, B., A comparative study of artificial bee colony algorithm, Appl. Math. Comput., 214, 108-132 (2009) · Zbl 1169.65053
[9] Singh, A., An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem, Appl. Soft Comput., 9, 625-631 (2009)
[10] Kang, F.; Li, J. J.; Xu, Q., Structural inverse analysis by hybrid simplex artificial bee colony algorithms, Comput. Struct., 87, 861-870 (2009)
[11] Samrat, L.; Udgata, S.; Abraham, A., Artificial bee colony algorithm for small signal model parameter extraction of MESFET, Eng. Appl. Artif. Intell., 11, 1573-2916 (2010)
[12] Storn, R.; Price, K., Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 23, 689-694 (2010)
[13] Tsai, P. W.; Pan, J. S.; Liao, B. Y.; Chu, S. C., Enhanced artificial bee colony optimization, Int. J. Innovative Comput. Appl., 5, 1-12 (2009)
[14] Zhu, G. P.; Kwong, S., Gbest-guided artificial bee colony algorithm for numerical function optimization, Appl. Math. Comput., 217, 3166-3173 (2010) · Zbl 1204.65074
[15] Banharnsakun, A.; Achalakul, T.; Sirinaovakul, B., The best-so-far selection in artificial bee colony algorithm, Appl. Soft Comput., 11, 2888-2901 (2010)
[16] B. Basturk, D. Karaboga, A modified artificial bee colony algorithm for real-parameter optimization, Inform. Sci., doi:10.1016/j.ins.2010.07.015; B. Basturk, D. Karaboga, A modified artificial bee colony algorithm for real-parameter optimization, Inform. Sci., doi:10.1016/j.ins.2010.07.015 · Zbl 1149.90186
[17] Kang, F.; J Li, J.; Ma, Z. Y., Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions, Inform. Sci., 12, 3508-3531 (2011) · Zbl 1242.65124
[18] Alatas, B., Chaotic bee colony algorithms for global numerical optimization, Expert Syst. Appl., 37, 5682-5687 (2010)
[19] Rahnamayan, S.; Tizhoosh, H. R.; Salama, M. A., Opposition-based differential evolution, IEEE Trans. Evol. Comput., 12, 64-79 (2008)
[20] Sundar, S.; Singh, A., An artificial bee colony algorithm for the 0-1 multidimensional knapsack problem, Commun. Comput. Inf. Sci., 94, 141-151 (2010) · Zbl 1206.90096
[21] S. Sundar, A. Singh, A hybrid heuristic for the set covering problem, Oper. Res. Int. J., doi:10.1007/s12351-010-0086-y; S. Sundar, A. Singh, A hybrid heuristic for the set covering problem, Oper. Res. Int. J., doi:10.1007/s12351-010-0086-y · Zbl 1273.90180
[22] Liu, J.; Zhong, W. C.; Jiao, L. C., An organizational evolutionary algorithm for numerical optimization, IEEE Trans. Syst. Man Cybern. B Cybern., 37, 1052-1064 (2007)
[23] Ratnaweera, A.; Halgamuge, S.; Watson, H., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Trans. Evol. Comput., 8, 240-255 (2004)
[24] Liang, J. J.; Qin, A. K.; Suganthan, P. N.; Baskar, S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Comput., 10, 281-295 (2006)
[25] Zhan, Z. H.; Zhang, J.; Li, Y.; Chung, S. H., Adaptive particle swarm optimization, IEEE Trans. Syst. Man Cybern. B Cybern., 39, 1362-1381 (2009)
[26] A.K. Qin, P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: Proc. IEEE Congr. Evol. Comput. Edinburgh, 2005, pp. 1785-1791.; A.K. Qin, P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: Proc. IEEE Congr. Evol. Comput. Edinburgh, 2005, pp. 1785-1791.
[27] Brest, J.; Greiner, S.; Borko, B.; Zumer, V., Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE Trans. Evol. Comput., 10, 646-657 (2006)
[28] Zhang, J. Q.; Sanderson, A., JADE: Adaptive differential evolution with optional external archive, IEEE Trans. Evol. Comput., 13, 945-958 (2009)
[29] Das, S.; Abraham, A.; Chakraborty, U. K.; Konar, A., Differential evolution using a neighborhood-based mutation operator, IEEE Trans. Evol. Comput., 13, 526-553 (2009)
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