Imperialist Competitive Algorithm with Adaptive Colonies Movement

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.

[1]  A. Griewank.  Generalized descent for global optimization , 1981 .

[2]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[3]  Hitoshi Iba,et al.  Linear and Combinatorial Optimizations by Estimation of Distribution Algorithms , 2002 .

[4]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[5]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: Part I-Basic Theory and Applications , 1999 .

[6]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[7]  Marcel Bergerman,et al.  Cultural algorithms: concepts and experiments , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[8]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[9]  Karim Faez,et al.  Neural Network Learning Based on Chaotic Imperialist Competitive Algorithm , 2010, 2010 2nd International Workshop on Intelligent Systems and Applications.

[10]  Franz J. Kurfess,et al.  Intelligent Systems and Applications , 2000 .

[11]  Lester Ingber,et al.  Simulated annealing: Practice versus theory , 1993 .

[12]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[13]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[14]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[15]  Karim Faez,et al.  Imperialist Competitive Algorithm Using Chaos Theory for Optimization (CICA) , 2010, 2010 12th International Conference on Computer Modelling and Simulation.

[16]  Bo Xing,et al.  Gravitational Search Algorithm , 2014 .

[17]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Farzad Rajaei Salmasi,et al.  Decentralized PID Controller Design for a MIMO Evaporator Based on Colonial Competitive Algorithm , 2008 .

[19]  Haralambos Sarimveis,et al.  A line up evolutionary algorithm for solving nonlinear constrained optimization problems , 2005, Comput. Oper. Res..

[20]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[21]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[22]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.