A Hybrid Proposed Imperialist Competitive Algorithm with Conjugate Gradient Approach for Large Scale Global Optimization
Keywords:
Large scale global Optimization, Evolutionary Algorithms, Imperialist Competitive Algorithm (ICA), Conjugate Gradient (CG)Abstract
This paper presents a novel hybrid imperialist competitive algorithm called ICA-CG algorithm. Such an algorithm combines the evolution ideas of the imperialist competitive algorithm and the classic optimization ideas of the conjugate gradient, based on the compensation for solving the large scale optimization. In the ICA-CG algorithm, the process of every iteration is divided into two stages. In the first stage, the randomly, rapidity and wholeness of the imperialist competitive Algorithm are used. In the second stage, one of the common optimization classical techniques, that called conjugate gradient to move imperialist countries, is used. Experimental results for five well known test problems have shown the superiority of the new ICA-CG algorithm, in large scale optimization, compared with the classical GA, ICA, PSO and ABC algorithms, with regard to the convergence of speed and quality of obtained solutions.
References
Andrei, N.(2007) .Scaled conjugate gradient algorithms for unconstrained optimization.Compute. Optima. Appl. 38. pp.401-416.
Andrei, N. (2008). An Unconstrained Optimization Test Function Collection. Adv. Model. Optimize. Vol. 10, pp. 147-161.
Atashpaz-Gargari, E., Lucas, C.(2007). Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition. In: IEEE Congress on Evolutionary Computation (CEC 2007). pp. 4661–4667
Atashpaz-Gargari, E., Hashemzadeh, F., Rajabioun, R., Lucas, C.(2008). Colonial Competitive Algorithm: A novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computing and Cybernetics (IJICC) 1(3).pp.337–355.
Bahrami, H., Feaz, K., Abdechiri, M. (2010), Imperialist Competitive Algorithm using Chaos Theory for Optimization (CICA). In: Proceedings of the 12th International Conference on Computer Modelling and Simulation .
Bahrami, H., Feaz, K., Abdechiri, M.(2010). Adaptive Imperialist Competitive Algorithm (AICA).In: Proceedings of The 9th IEEE International Conference on Cognitive Informatics. ICCI 2010.
Ghodrati , A., Malakooti, V., Soleimani, M.(2012). A Hybrid ICA/PSO Algorithm by Adding Independent Countries for Large Scale Global Optimization. In: Proceedings of The Forth Asian Conference of Intelligent Information and Database Systems . ACIIDS 2012. Part III. LNAI 7198, pp. 99–1087.
Goldberg, D.E.(1989). Genetic Algorithms in Search. Optimization & Machine Learning. Addison-Wesley. Reading.
Holland, J.H.(1990). ECHO: Explorations of Evolution in a Miniature World. In: Farmer, J.D.,Doyne, J. (eds.) Proceedings of the Second Conference on Artificial Life .
Karaboga, D., Basturk, B.(2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3). pp.459–471
Kennedy, J., Eberhart, R.C.(1995). Particle Swarm Optimization. Proceedings of IEEE. pp 1942–1948 .
Patel, R.V. (2000). System Optimization. Dept. of Electrical and Computer Engineering University of Western Ontario London. Ontario. Canada.
Ramezani, F.,Lotfi , S., Soltani, M.A.(2012). A Hybrid Evolutionary Imperialist Competitive Algorithm (HEICA) . In: Proceedings of The Forth Asian Conference of Intelligent Information and Database Systems. ACIIDS 2012.Part I. LNAI 7196. pp. 359–368
Sarimveis, H., Nikolakopoulos, A.(2005). A Life Up Evolutionary Algorithm for Solving Nonlinear Constrained Optimization Problems. Computer & Operation Research 32(6). pp.1499–1514.
Srinivasan, D., Seow, T.H.(2003). Evolutionary Computation. In: CEC 2003.December 8-12. vol. 4. pp. 2292–2297. Canberra. Australia.
Soltani, M.A., Lotfi ,S.,Ramezani, F.(2012). Quad Countries Algorithm (QCA). In: Proceedings of The Forth Asian Conference of Intelligent Information and Database Systems . ACIIDS 2012. Part III. LNAI 7198. pp. 119–129.
Sun, L. and Zhang, W. (2006). An Acceleration Micro Genetic Algorithm for Numerical Optimization. Lecture Notes in computer Science. Vol. 4247.pp 277-283.
Mühlenbein, H., Schomisch, M., Born, J.(1991). The Parallel Genetic Algorithm as Function Optimizer. In: Proceedings of The Forth International Conference on Genetic Algorithms, pp. 270–278. University of California. San diego.
Zhang, Y., Wang, Y., Peng, C.(2009).Improved Imperialist Competitive Algorithm for Constrained Optimization. International Forum on Computer Science-Technology and Applications.
Andrei, N. (2008). An Unconstrained Optimization Test Function Collection. Adv. Model. Optimize. Vol. 10, pp. 147-161.
Atashpaz-Gargari, E., Lucas, C.(2007). Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition. In: IEEE Congress on Evolutionary Computation (CEC 2007). pp. 4661–4667
Atashpaz-Gargari, E., Hashemzadeh, F., Rajabioun, R., Lucas, C.(2008). Colonial Competitive Algorithm: A novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computing and Cybernetics (IJICC) 1(3).pp.337–355.
Bahrami, H., Feaz, K., Abdechiri, M. (2010), Imperialist Competitive Algorithm using Chaos Theory for Optimization (CICA). In: Proceedings of the 12th International Conference on Computer Modelling and Simulation .
Bahrami, H., Feaz, K., Abdechiri, M.(2010). Adaptive Imperialist Competitive Algorithm (AICA).In: Proceedings of The 9th IEEE International Conference on Cognitive Informatics. ICCI 2010.
Ghodrati , A., Malakooti, V., Soleimani, M.(2012). A Hybrid ICA/PSO Algorithm by Adding Independent Countries for Large Scale Global Optimization. In: Proceedings of The Forth Asian Conference of Intelligent Information and Database Systems . ACIIDS 2012. Part III. LNAI 7198, pp. 99–1087.
Goldberg, D.E.(1989). Genetic Algorithms in Search. Optimization & Machine Learning. Addison-Wesley. Reading.
Holland, J.H.(1990). ECHO: Explorations of Evolution in a Miniature World. In: Farmer, J.D.,Doyne, J. (eds.) Proceedings of the Second Conference on Artificial Life .
Karaboga, D., Basturk, B.(2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3). pp.459–471
Kennedy, J., Eberhart, R.C.(1995). Particle Swarm Optimization. Proceedings of IEEE. pp 1942–1948 .
Patel, R.V. (2000). System Optimization. Dept. of Electrical and Computer Engineering University of Western Ontario London. Ontario. Canada.
Ramezani, F.,Lotfi , S., Soltani, M.A.(2012). A Hybrid Evolutionary Imperialist Competitive Algorithm (HEICA) . In: Proceedings of The Forth Asian Conference of Intelligent Information and Database Systems. ACIIDS 2012.Part I. LNAI 7196. pp. 359–368
Sarimveis, H., Nikolakopoulos, A.(2005). A Life Up Evolutionary Algorithm for Solving Nonlinear Constrained Optimization Problems. Computer & Operation Research 32(6). pp.1499–1514.
Srinivasan, D., Seow, T.H.(2003). Evolutionary Computation. In: CEC 2003.December 8-12. vol. 4. pp. 2292–2297. Canberra. Australia.
Soltani, M.A., Lotfi ,S.,Ramezani, F.(2012). Quad Countries Algorithm (QCA). In: Proceedings of The Forth Asian Conference of Intelligent Information and Database Systems . ACIIDS 2012. Part III. LNAI 7198. pp. 119–129.
Sun, L. and Zhang, W. (2006). An Acceleration Micro Genetic Algorithm for Numerical Optimization. Lecture Notes in computer Science. Vol. 4247.pp 277-283.
Mühlenbein, H., Schomisch, M., Born, J.(1991). The Parallel Genetic Algorithm as Function Optimizer. In: Proceedings of The Forth International Conference on Genetic Algorithms, pp. 270–278. University of California. San diego.
Zhang, Y., Wang, Y., Peng, C.(2009).Improved Imperialist Competitive Algorithm for Constrained Optimization. International Forum on Computer Science-Technology and Applications.
Downloads
Published
2014-06-30
How to Cite
Mitras, B. A., & Sultan, J. A. (2014). A Hybrid Proposed Imperialist Competitive Algorithm with Conjugate Gradient Approach for Large Scale Global Optimization. Science Journal of University of Zakho, 2(1), 184–195. Retrieved from https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/151
Issue
Section
Science Journal of University of Zakho
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work, with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online.
