Abstract
This paper presents the hybrid approach of Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) for global optimization. In standard ICA, there are only two types of countries: imperialists and colonies. In the proposed hybrid algorithm (ICA/PSO) we added another type of country, ‘Independent’. Independent countries do not fall into the category of empires, and are anti-imperialism. In addition, they are united and their shared goal is to get stronger in order to rescue colonies and help them join independent countries. These independent countries are aware of each other positions and make use of swarm intelligence in PSO for their own progress. Experimental results are examined with benchmark functions provided by CEC2010 Special Session on Large Scale Global Optimization (LSGO) and the results are compared with some previous LSGO algorithms, standard PSO and standard ICA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Talbi, E.G.: Metaheuristic: from design to implementation. Wiley Publishing, Hoboken (2009)
Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley Publishing, New Jersey (2010)
Yang, X.-S.: Metaheuristic Optimization: Algorithm Analysis and Open Problems. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 21–32. Springer, Heidelberg (2011)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, pp. 1942–1948 (1995)
Puranik, P., Bajaj, P., Abraham, A., Palsodkar, P., Deshmukh, A.: Human Perception-based Color Image Segmentation Using Comprehensive Learning Particle Swarm Optimization. Journal of Information Hiding and Multimedia Signal Processing 2(3), 227–235 (2011)
Chang, F.C., Huang, H.-C.: A Refactoring Method for Cache-Efficient Swarm Intelligence Algorithms. Information Sciences, doi:10.1016/j.ins.2010.02.025
Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, pp. 4661–4667 (2007)
Nedjah, N., Mourelle, L.M.: Swarm Intelligent Systems. Springer, New York (2006)
Tang, K., et al.: Benchmark Functions for the CEC 2010 Special Session and Competition on Large-Scale Global Optimization: Nature Inspired Computation and Applications Laboratory Technical report (2010), http://nical.ustc.edu.cn/cec10ss.php
Brest, J., Zamuda, A., Fister, I., Maučec, M.S.: Large Scale Global Optimization using Self-adaptive Differential Evolution Algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC). IEEE Press, Barcelona (2010)
Wang, H., Wu, Z., Rahnamayan, S., Jiang, D.: Sequential DE Enhanced by Neighborhood Search for Large Scale Global Optimization. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC). IEEE Press, Barcelona (2010)
Omidvar, M.N., Li, X., Yao, X.: Cooperative Co-evolution with Delta Grouping for Large Scale Non-separable Function Optimization. In: Proceedings of IEEE Congress onEvolutionary Computation (CEC), pp. 1762–1769. IEEE Press, Barcelona (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ghodrati, A., Malakooti, M.V., Soleimani, M. (2012). A Hybrid ICA/PSO Algorithm by Adding Independent Countries for Large Scale Global Optimization. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28493-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-28493-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28492-2
Online ISBN: 978-3-642-28493-9
eBook Packages: Computer ScienceComputer Science (R0)