Imperialist Competitive Algorithm with Adaptive Colonies Movement

Full Text (PDF, 368KB), PP.49-57

Views: 0 Downloads: 0


Helena Bahrami 1,* Marjan Abdechiri 1,2 Mohammad Reza Meybodi 3

1. Dept. of Electronic, Computer and IT, Qazvin Azad University, Qazvin, Iran

2. Young Researchers Club, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran

3. Dept. of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran

* Corresponding author.


Received: 12 Apr. 2011 / Revised: 7 Aug. 2011 / Accepted: 6 Oct. 2011 / Published: 8 Mar. 2012

Index Terms

Imperialist Competitive Algorithm, Absorption Policy, Density Probabilistic Model, Evolutionary Algorithm


The novel Imperialist Competitive Algorithm (ICA) that was recently introduced has a good performance in some optimization problems. The ICA inspired by socio-political process of imperialistic competition of human being in the real world. In this paper, a new Imperialist Competitive Algorithm with Adaptive Radius of Colonies movement (ICAR) is proposed. In the proposed algorithm, for an effective search, the Absorption Policy changed dynamically to adapt the radius of colonies movement towards imperialist’s position. The ICA is easily stuck into a local optimum when solves high-dimensional multi-modal numerical optimization problems. To overcome this shortcoming, we use probabilistic model that utilize the information of colonies positions to balance the exploration and exploitation abilities of the Imperialist Competitive Algorithm. Using this mechanism, ICA exploration capability will enhance. Some famous unconstraint benchmark functions used to test the ICAR performance. Simulation results show this strategy can improve the performance of the ICA algorithm significantly.

Cite This Paper

Helena Bahrami, Marjan Abdechiri, Mohammad Reza Meybodi, "Imperialist Competitive Algorithm with Adaptive Colonies Movement", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.2, pp.49-57, 2012. DOI:10.5815/ijisa.2012.02.06


[1]H. Sarimveis and A. Nikolakopoulos, “A Line Up Evolutionary Algorithm for Solving Nonlinear Constrained Optimization Problems,” Computers & Operations Research, 32(6):pp.1499–1514, 2005.

[2]M. Melanie, “An Introduction to Genetic Algorithms,” Massachusett's MIT Press, 34(7):pp.1-9, 1999.

[3]J. Kennedy and R.C. Eberhart, “Particle swarm optimization,” in: Proceedings of IEEE International Conference on Neural Networks, Piscataway: IEEE, pp. 1942–1948, 1995.

[4]L. A. Ingber, “Simulated annealing: practice versus theory,” J. Math. Comput. Modell., 18(11):pp. 29–57, 1993.

[5]B. Franklin and M. Bergerman, “Cultural Algorithms: Concepts and Experiments,” In Proceedings of the IEEE Congress on Evolutionary Computation, 2: pp. 1245–1251, 2000.

[6]M. Dorigo, V. Maniezzo and A. Colorni, “The ant system: optimization by a colony of cooperating agents,” IEEE Transaction System Man Cybern, B 26(1):pp. 29–41, 1996.

[7]R. Storn and K. Price, “Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, 11(4):pp. 341–359, 1997.

[8]K. Lee and Z. Geem, “A new structural optimization method based on the harmony search algorithm,” Computers and Structures, 82:781-98, 2004.

[9]F. J. Von Zuben and L. N. De Castro, “Artificial Immune Systems: Part I - Basic Theory and Applications,” School of Computing and Electrical Engineering, State University of Campinas, Brazil, Technical Report DCA-RT 01/99, 1999.

[10]A. Kaveh, S. Talatahari “A novel heuristic optimization method: Charged system search,” Acta Mechanica, doi:10.1007/s00707-009-0270-4.

[11]E. Rashedi, H. Nezamabadi-pour and S. Saryazdi, “A Gravitational Search Algorithm,” Information Science, Special Section on High Order Fuzzy Sets, 179(13): pp. 2232-2248, 2009.

[12]E. Atashpaz-Gargari and C. Lucas, “Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition,” IEEE Congress on Evolutionary Computation (CEC 2007), pp 4661-4667, 2007.

[13]H. Bahrami, K. Faez, M. Abdechiri, “Imperialist Competitive Algorithm using Chaos Theory for Optimization,” UKSim-AMSS 12th International Conference on Computer Modeling and Simulation, 2010.

[14]R. Rajabioun, F. Hashemzadeh, E. Atashpaz-Gargari, B. Mesgari and F. Rajaei Salmasi, "Identification of a MIMO Evaporator and Its Decentralized PID Controller Tuning Using Colonial Competitive Algorithm", Accepted to be presented in IFAC World Congress, 2008.

[15]A. Biabangard-Oskouyi, E. Atashpaz-Gargari, N. Soltani and C. Lucas, “Application of Imperialist Competitive Algorithm for materials property characterization from sharp indentation test,” International Journal of Engineering Simulation, under revision, 1(3):pp. 337-355, 2008.

[16]M. Abdechiri, K. Faez and H. Bahrami, “Neural Network Learning based on Chaotic Imperialist Competitive Algorithm,” The 2nd International Workshop on Intelligent System and Applications (ISA2010), 2010.

[17]L. Rastrigin, “External control systems,” In Theoretical Foundations of engineering cybernetics series. Moscow, Russian, Nauka, 1974.

[18]A. Griewangk “Generalized descent of global optimization,” Optim. Theor. Appl., 34: pp. 11–39, 1981.

[19]A. Papoulis, ”Probability Random Variables and Stochastic Processes,” McGraw-Hill, 1965.

[20]R. C. Smith and P. Cheeseman, ”On the Representation and Estimation of Spatial Uncertainty,” the International Journal of Robotics Research, 5(4), Winter 1986.

[21]T. K. Paul and H. Iba, “Linear and Combinatorial Optimizations by Estimation of Distribution Algorithms,” 9th MPS Symposium on Evolutionary Computation, IPSJ, Japan, 2002.

[22]Y. Bar-Shalom, X. Rong Li, and T. Kirubarajan, ”Estimation with Applications to Tracking and Navigation,” John Wiley & Sons, 2001.