Abstract
To solve high-dimensional function optimization problems, many evolutionary algorithms have been proposed. In this paper, we propose a new cooperative coevolution orthogonal artificial bee colony (CCOABC) algorithm in an attempt to address the issue effectively. Cooperative coevolution frame, a popular technique in evolutionary algorithms for large scale optimization problems, is adopted in this paper. This frame decomposes the problem into several subcomponents by random grouping, which is a novel decomposition strategy mainly for tackling nonseparable functions. This strategy can increase the probability of grouping interacting variables in one subcomponent. And for each subcomponent, an improved artificial bee colony (ABC) algorithm, orthogonal ABC, is employed as the subcomponent optimizer. In orthogonal ABC, an Orthogonal Experimental Design method is used to let ABC evolve in a quick and efficient way. The algorithm has been evaluated on standard high-dimensional benchmark functions. Compared with other four state-of-art evolutionary algorithms, the simulation results demonstrate that CCOABC is a highly competitive algorithm for solving high-dimensional function optimization problems.
Similar content being viewed by others
References
Bi XJ, Wang YJ (2011) An improved artificial bee colony algorithm. In: Proceedings of the 2011 third international conference on computer research and development (ICCRD), pp 174–177
dos Santos Coelho L, Alotto P (2011) Gaussian artificial bee colony algorithm approach applied to Loney’s solenoid benchmark problem. IEEE Trans Magn 47(5):1326–1329
Hu XM, Zhang J, Li Y (2008) Orthogonal methods based ant colony search for solving continuous optimization problems. J Comput Sci Tech 23(1):2–18
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Leung YW, Wang YP (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans Evol Comput 5(1):41–53
Li XD, Yao X (2011) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evol Comput 16(2):210–224
Math Stat Res Group, Chinese Acad Sci (1975) Orthogonal design (in Chinese). People Education Pub, Beijing
Molina D, Lozano M, Herrera F (2010) MA-SW-Chains: memetic algorithm based on local search chains for large scale continuous global optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 1–8
Montgomery DC (2000) Design and analysis of experiments, 5th edn. Wiley, New York
Omidvar MN, Li XD, Yao X (2010) Cooperative co-evolution with delta grouping for large scale non-separable function optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 1–8
Potter MA, De Jong KA (1994) A cooperative coevolutionary approach to function optimization. In: Proceedings of the third conference on parallel problem solving nature, pp 249–257
Sharma TK, Pant M (2011) Enhancing the food locations in an artificial bee colony algorithm. In: Proceedings of IEEE symposium on swarm intelligence, pp 1–5
Shu LS, Ho SY, Ho SJ (2004) A novel orthogonal simulated annealing algorithm for optimization of electromagnetic problems. IEEE Trans Magn 40(4):1791–1795
Tang K, Yao X, Suganthan PN, MacNish C, Chen YP, Chen CM, Yang Z (2007) Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Tech Rep, Nature Inspired Computation and Applications Laboratory, USTC, China. http://nical.ustc.edu.cn/cec08ss.php
Tang K, Li X, Suganthan PN, Yang Z, Weise T (2009) Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization. Tech Rep, Nature Inspired Computation and Applications Laboratory, USTC, China. http://nical.ustc.edu.cn/cec10ss.php
Wang H, Rahnamayan S, Wu ZJ (2011) Adaptive differential evolution with variable population size for solving high-dimensional problems. In: Proceedings of IEEE congress on evolutionary computation, pp 2626–2632
Yang ZY, Tang K, Yao X (2008a) Multilevel cooperative coevolution for large scale optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 1663–1670
Yang ZY, Tang K, Yao X (2008b) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999
Zhan ZH, Zhang J, Li Y, Shi YH (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847
Zhang DL, Guan XP, Tang YG, Tang Y (2011) Modified artificial bee colony algorithms for numerical optimization. In: Proceedings of the 2011 third international workshop on intelligent systems and applications, pp 1–4
Zhang QF, Leung YW (1999) An orthogonal genetic algorithm for multimedia multicast routing. IEEE Trans Evol Comput 3(1):53–62
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by A-A. Tantar.
Rights and permissions
About this article
Cite this article
Ren, Y., Wu, Y. An efficient algorithm for high-dimensional function optimization. Soft Comput 17, 995–1004 (2013). https://doi.org/10.1007/s00500-013-0984-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-013-0984-z