Abstract
The following paper introduces a hybrid algorithm that combines Artificial Bee Colony Algorithm (ABC) and a model of Evolution Strategies (ES) found in the Evolutionary Particle Swarm Optimization (EPSO), another hybrid metaheuristic. The goal of this approach is to incorporate the effectiveness and simplicity of the ABC with the thorough local search mechanism of the Evolution Strategies in order to devise an algorithm that is able to achieve better optimality in less time than the original ABC applied to function optimization problems. With the intention of assessing this novel algorithm performance and reliability, several unconstrained benchmark functions as well as four large-scale constrained optimization-engineering problems (WBD, DPV, SRD-11 and MWTCS) act as an evaluation environment. The results obtained by the ABC+ES are compared to original ABC and several other optimization techniques.
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References
Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report, Erciyes University, Kayseri (2005)
Akay, B., Karaboga, D.: Artificial bee colony algorithm for large-scale problems and engineering design optimization. J. Intell. Manuf. 23(4), 1001–1014 (2012)
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)
Karaboga, D., et al.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)
Karaboga, D., Basturk, D., Ozturk, C.: Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: Modeling Decisions for Artificial Intelligence, vol. 4617, pp. 318–319. Springer, Berlin (2009)
Karaboga, D., Ozturk, C.: Neural networks training by artificial bee colony algorithm on pattern classification. Neural Netw. World 19(3), 279–292 (2009)
Miranda, V., Fonseca, N.: EPSO—evolutionary particle swarm optimization, a new algorithm with applications in power systems. In: IEEE/PES Transmission and Distribution Conference and Exhibition 2002: Asia Pacific, vol. 2, pp. 745–750. IEEE Press, New York (2002)
Pham, D.T. et al.: The bees algorithm. Technical Report. Tech. rep. Manufacturing Engineering Centre, Cardiff University, UK (2005)
Tereshko, V., Loengarov, A.: Collective decision-making in honey bee foraging dynamics. Comput. Inf. Syst. 9(3), 1–7 (2005)
Karaboga, D., Ozturk, C.: Hybrid artificial bee colony algorithm for neural network training. Appl. Intell. Data Anal. (2011)
Apalak, M.K., Karaboga, D., Akay, B.: The artificial bee colony algorithm in layer optimization for the maximum fundamental frequency of symmetrical laminated composite plates. Eng. Optim. 46(3), 420–437 (2014)
Miranda, V., Keko, H., Duque, A.J.: Stochastic star communication topology in evolutionary particle swarm optimization (EPSO). IJ-CIR Int. J. Comput. Intell. Res. 4(2), 105–116 (2007)
Naing, O.W.: A comparison study on particle swarm and evolutionary particle swarm optimization using capacitor placement problem. In: 2nd IEEE International Conference on Power and Energy (PECon 08) (2008)
Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166–3173 (2010)
Mollinetti, M.A.F., Souza, D.L., Teixeira, O.N.: ABC+ES: a novel hybrid artificial bee colony algorithm with evolution strategies. In: Proceedings of the 2014 Conference Companion on Genetic and Evolutionary Computation Companion: GECCO Comp’14, pp. 1463–1464. ACM, New York (2014)
Brajevic, I., Tuba, M., Subotic, M.: Improved artificial bee colony algorithm for constrained problems. In: Proceedings of the 11th WSEAS International Conference on Neural Networks, Fuzzy Systems and Evolutionary Computing, pp. 185–190 (2010)
Teixeira, O.N. et al.: Genetic algorithm with social interaction for constrained optimization problems. In: Editora OMNIPAX (Chap. 10), 1st edn, pp. 197–223 (2011)
He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell. 20(1), 89–99 (2007)
Coello Coello, C., Montes, E.: Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv. Eng. Inf. 16(3), 193–203 (2002)
Cagnina, L., Esquivel, S., Coello Coello, C.: Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32(3), 319–326 (2008)
Benala, T.R. et al.: A novel approach to image edge enhancement using artificial bee colony optimization algorithm for hybridized smoothening filters. In: Abrahan, A. et al. (ed.) World Congress on Nature and Biologically Inspired Computing, pp. 1070–1075 (2009)
Coelho, L.S.: Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst. Appl. 37(2), 1676–1683 (2010)
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Mollinetti, M.A.F., Souza, D.L., Pereira, R.L., Yasojima, E.K.K., Teixeira, O.N. (2016). ABC+ES: Combining Artificial Bee Colony Algorithm and Evolution Strategies on Engineering Design Problems and Benchmark Functions. In: Abraham, A., Han, S., Al-Sharhan, S., Liu, H. (eds) Hybrid Intelligent Systems. HIS 2016. Advances in Intelligent Systems and Computing, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-319-27221-4_5
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DOI: https://doi.org/10.1007/978-3-319-27221-4_5
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