Article

A hybrid ICA/PSO algorithm by adding independent countries for large scale global optimization

Authors:

Amirhossein Ghodrati,

Mohammad V. Malakooti,

Mansooreh SoleimaniAuthors Info & Claims

ACIIDS'12: Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III

Pages 99 - 108

https://doi.org/10.1007/978-3-642-28493-9_12

Published: 19 March 2012 Publication History

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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.

References

[1]

Talbi, E. G.: Metaheuristic: from design to implementation. Wiley Publishing, Hoboken (2009)

Digital Library

Google Scholar

[2]

Yang, X. S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley Publishing, New Jersey (2010)

Digital Library

Google Scholar

[3]

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)

Digital Library

Google Scholar

[4]

Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, pp. 1942-1948 (1995)

Crossref

Google Scholar

[5]

Puranik, P., Bajaj, P., Abraham, A., Palsodkar, P., Deshmukh, A.: Human Perceptionbased Color Image Segmentation Using Comprehensive Learning Particle Swarm Optimization. Journal of Information Hiding and Multimedia Signal Processing 2(3), 227-235 (2011)

Google Scholar

[6]

Chang, F.C., Huang, H.-C.: A Refactoring Method for Cache-Efficient Swarm Intelligence Algorithms. Information Sciences.

Digital Library

Google Scholar

[7]

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)

Crossref

Google Scholar

[8]

Nedjah, N., Mourelle, L. M.: Swarm Intelligent Systems. Springer, New York (2006)

Google Scholar

[9]

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

Google Scholar

[10]

Brest, J., Zamuda, A., Fister, I., Maucec, 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)

Google Scholar

[11]

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)

Google Scholar

[12]

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)

Google Scholar

Cited By

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Huang HLv LYe SHao Z(2019)Particle swarm optimization with convergence speed controller for large-scale numerical optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3098-923:12(4421-4437)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3098-9

A hybrid ICA/PSO algorithm by adding independent countries for large scale global optimization
1. Information systems
  1. Data management systems
    1. Database administration
2. Theory of computation
  1. Design and analysis of algorithms

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Published In

ACIIDS'12: Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III

March 2012

523 pages

ISBN:9783642284922

Editors:
Jeng-Shyang Pan
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien-Kung Road, Kaohsiung, Taiwan
,
Shyi-Ming Chen
Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, No. 140, Min-Shen Road, Taichung, Taiwan
,
Ngoc Thanh Nguyen
Graduate Institute of Educational Measurement and Statistics, Wrocław University of Technology, Wyb. Wyspiańskiego 27, Wrocław, Poland

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Springer-Verlag

Berlin, Heidelberg

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Published: 19 March 2012

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Huang HLv LYe SHao Z(2019)Particle swarm optimization with convergence speed controller for large-scale numerical optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3098-923:12(4421-4437)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3098-9

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Opposition based initialization in particle swarm optimization (O-PSO)

A Modified Quantum-Behaved Particle Swarm Optimization

Large-scale global optimization based on hybrid swarm intelligence algorithm

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