A Hybrid Evolutionary Imperialist Competitive Algorithm (HEICA)

This paper proposes a new approach by combining the Evolutionary Algorithm and Imperialist Competitive Algorithm. This approach tries to capture several people involved in community development characteristic. People live in different type of communities: Monarchy, Republic andAutocracy. People dominion is different in each community. Research work has been undertaken to deal with curse of dimensionality and to improve the convergence speed and accuracy of the basic ICA and EA algorithms. Common benchmark functions and large scale global optimization have been used to compare HEICA with ICA, EA, PSO, ABC, SDENS and jDElsgo. HEICA indeed has established superiority over the basic algorithms with respect to set of functions considered and it can be employed to solve other global optimization problems, easily. The results show the efficiency and capabilities of the new hybrid algorithm in finding the optimum. Amazingly, its performance is about 85% better than others. The performance achieved is quite satisfactory and promising.

[1]  Farzad Razavi,et al.  A New Hybrid Evolutionary Algorithm Based on ICA and GA: Recursive-ICA-GA , 2010, IC-AI.

[2]  Farzad Razavi,et al.  Using Evolutionary Imperialist Competitive Algorithm ( ICA ) to Coordinate Overcurrent Relays , 2011 .

[3]  Zhijian Wu,et al.  Sequential DE enhanced by neighborhood search for Large Scale Global Optimization , 2010, IEEE Congress on Evolutionary Computation.

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

[5]  Xiaodong Li,et al.  Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale , 2009 .

[6]  Ponnuthurai N. Suganthan,et al.  Multi-objective optimization using self-adaptive differential evolution algorithm , 2009, 2009 IEEE Congress on Evolutionary Computation.

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

[8]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[9]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[10]  M. J. Nigam,et al.  Synergy of evolutionary algorithm and socio-political process for global optimization , 2010, Expert Syst. Appl..

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Witold Pedrycz,et al.  Foundations of Fuzzy Logic and Soft Computing, 12th International Fuzzy Systems Association World Congress, IFSA 2007, Cancun, Mexico, June 18-21, 2007, Proceedings , 2007, IFSA.

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

[14]  Marjan Abdechiri,et al.  A Hybrid Hopfield Network-Imperialist Competitive Algorithm for Solving the SAT Problem , 2011 .

[15]  Janez Brest,et al.  Large scale global optimization using self-adaptive differential evolution algorithm , 2010, IEEE Congress on Evolutionary Computation.

[16]  Caro Lucas,et al.  A hybrid IWO/PSO algorithm for fast and global optimization , 2009, IEEE EUROCON 2009.