Abstract
Artificial bee colony (ABC) algorithm developed by Karaboga is a nature inspired metaheuristic based on honey bee foraging behavior. It was successfully applied to continuous unconstrained optimization problems and later it was extended to constrained design problems as well. This paper introduces an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems. Our UABC algorithm enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm. This upgraded algorithm has been implemented and tested on standard engineering benchmark problems and the performance was compared to the performance of the latest Akay and Karaboga’s ABC algorithm. Our numerical results show that the proposed UABC algorithm produces better or equal best and average solutions in less evaluations in all cases.
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References
Akay, B., & Karaboga, D. (2010). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0393-4 (Published online).
Aydin, M. E. (2010), Coordinating metaheuristic agents with swarm intelligence. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0435-y (Published online).
Baykasoglu A., Ozbakir L., Tapkan P. (2007) Artificial bee colony algorithm and its application to generalized assignment problem. In: Chan F. T. S, Tiwari M. K. (eds) Swarm intelligence, focus on ant and particle swarm optimization. I-Tech Education and Publishing, Vienna, pp 113–144
Chang, F. C., & Huang, H. C. (2010). A refactoring method for cache-efficient swarm intelligence algorithms. Information Sciences doi:10.1016/j.ins.2010.02.025 (Article in press).
Cheshmehgaz, H. R., Desa, M. I., & Wibowo, A. (2011). A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0584-7 (Published online).
Deb K. (2000) An efficient constraint-handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2–4): 311–338
Dorigo M., Gambardella L. M. (1997) Ant colonies for the travelling salesman problem. Biosystems 43(2): 73–81
Gaitonde, V. N., & Karnik, S. R. (2010). Minimizing burr size in drilling using artificial neural network (ann)-particle swarm optimization (pso) approach. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0481-5 (Published online).
Haddad O. B., Afshar A., Marino M. A. (2006) Honey-bees mating optimization (hbmo) algorithm: A new heuristic approach for water resources optimization. Water Resources Management 20(5): 661–680
Hamida, S. B., & Schoenauer, M. (2002). Aschea: New results using adaptive segregational constraint handling. In Proceedings of the congress on evolutionary computation 2002 (CEC’2002) (pp. 884–889). IEEE Service Center.
He S., Prempain E., Wu Q. (2004) An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization 36(5): 585–605
Jovanovic R., Tuba M. (2011) An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem. Applied Soft Computing 11(8): 5360–5366
Jun-Qing Li Q. K. P, Xie S. X. (2010) A hybrid variable neighborhood search algorithm for solving multi-objective flexible job shop problems. Computer Science and Information Systems 7(4): 907–930
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department.
Karaboga D., Akay B. (2011) A modified artificial bee colony (abc) algorithm for constrained optimization problems. Applied Soft Computing 11(3): 3021–3031
Karaboga, D., & Basturk, B. (2007a). Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In LNAI 4529: IFSA’07 proceedings of the 12th international fuzzy systems association world congress on foundations of fuzzy logic and soft computing (pp. 789–798). Springer.
Karaboga D., Basturk B. (2007) A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm. Journal of Global Optimization 39(3): 459–471
Karaboga D., Basturk B. (2008) On the performance of artificial bee colony (abc) algorithm. Applied Soft Computing 8(1): 687–697
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE international conference on neural networks (pp. 1942–1948). Piscataway, NJ: IEEE Service Center.
Koziel S., Michalewicz Z. (1999) Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evolutionary Computation 7(1): 19–44
Lu, M., & Romanowski, R. (2011). Multi-contextual ant colony optimization of intermediate dynamic job shop problems. The International Journal of Advanced Manufacturing Technology 1–15. doi:10.1007/s00170-011-3634-6 (Published online).
Mezura-Montes, E., & Coello Coello, C. A. (2005). Useful infeasible solutions in engineering optimization with evolutionary algorithms. In MICAI 2005: Advances in artificial intelligence of lecture notes in computer science (pp. 652–662). Springer.
Mezura-Montes E., Miranda-Varela C. A. (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation 9(1): 1–17
Mezura-Montes E., Coello Coello E. M., Gomez-Ramon R. (2010) Differential evolution in constrained numerical optimization: An empirical study. Information Sciences 180(22): 4223–4262
Pan D., Liu Z. (2011) An improved particle swarm optimization algorithm. In: Deng H., Miao D., Wang F. L., Lei J. (eds) Emerging research in artificial intelligence and computational intelligence, communications in computer and information science. Springer, Berlin, pp 550–556
Parsopoulos, K., & Vrahatis, M. (2005). Unified particle swarm optimization for solving constrained engineering optimization problems. In ICNC 2005: Advances in natural computation, volume 3612/2005 of LCNS (pp. 582–591). Springer.
Pasandideh, S. H. R., Niaki, S. T. A., & Hajipour, V. (2011). A multi-objective facility location model with batch arrivals: Two parameter-tuned meta-heuristic algorithms. Journal of Intelligent Manufacturing. doi:10.1007/s10845-011-0592-7 (Published online).
Pham, D. T., Kog, E., Ghanbarzadeh, A., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In IPROMS 2006 proceeding 2nd international virtual conference on intelligent production machines and systems (pp. 454–459). Elsevier.
Puranik P., Bajaj P., Abraham A., Palsodkar P., Deshmukh A. (2011) Human perception-based color image segmentation using comprehensive learning particle swarm optimization. Journal of Information Hiding and Multimedia Signal Processing 2(3): 227–235
Ray T., Liew K. (2003) Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation 7(4): 386–396
Runarsson T. P., Yao X. (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on evolutionary computation 4(3): 284–294
Runarsson T. P., Yao X. (2005) Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man, and Cybernetics Part C-Applications and Reviews 35(2): 233–243
Srinivasan, D., & Seow, T. (2003). Particle swarm inspired evolutionary algorithm (ps-ea) for multiobjective optimization problems. In: The 2003 congress on evolutionary computation—CEC 2003 (pp. 2292–2297). IEEE Press.
Wang Y., Zhang B., Chen Y. (2011) Robust airfoil optimization based on improved particle swarm optimization method. Applied Mathematics and Mechanics (English Edition) 32(10): 1245–1254
Yang, X. S. (2005). Engineering optimizations via nature-inspired virtual bee algorithms. In Artificial intelligence and knowledge engineering applications: A bioinspired approach, LNCS (Vol. 3562, pp. 317–323). Springer.
Zavala, A. E. M., Hernandez, A., & Diharce, E. R. V. (2005). Constrained optimization via particle evolutionary swarm optimization algorithm (peso). In GECCO ’05 Proceedings of the 2005 conference on genetic and evolutionary computation (pp. 209–216). ACM Press.
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This research is supported by Ministry of Education and Science of Republic of Serbia, Grant No. III-44006.
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Brajevic, I., Tuba, M. An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. J Intell Manuf 24, 729–740 (2013). https://doi.org/10.1007/s10845-011-0621-6
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DOI: https://doi.org/10.1007/s10845-011-0621-6