Document Type: Original Research Paper


1 Artificial Intelligence Department, Islamic Azad University, Science and Research Branch, Tehran, Iran.

2 Faculty of Electrical Engineering,Control Department,K. N. Toosi University of Tech., Tehran, 19697, Iran.

3 Faculty of the Department of Mechatronics,K. N. Toosi University of Tech., Tehran, 19697, Iran.


This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule based system is optimized using Genetic Algorithm (GA). The proposed modified CA algorithm is compared with several other optimization algorithms including GA, particle swarm optimization (PSO), especially standard version of cultural algorithm. The obtained results demonstrate that the proposed modification enhances the performance of the CA in terms of global optimality.
Optimization is an important issue in different scientific applications. Many researches dedicated to algorithms that can be used to find an optimal solution for different applications. Intelligence optimizations which are generally classified as, evolutionary computations techniques like Genetic Algorithm, evolutionary strategy, and evolutionary programming, and swarm intelligence algorithms like particle swarm intelligence algorithm and ant colony optimization, etc are powerful tools for solving optimization problems


Main Subjects

[1] D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning”. Reading, MA: Addison-Wesley, 1989.
[2] Fogel, D. B. “Evolving Artificial Intelligence. “ Ph. D. Thesis, San Diego, CA: University of California.1992
[3] J. Kennedy and R.C. Eberhart, “Particle swarm optimization,” in Proceeding of the IEEE International Conference on Neural Networks, Perth, Australia, IEEE Service Center, pp. 12–13, 1995.
[4] M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: Optimization by a colony of cooperating agents,” in IEEE Transactions on Systems, Man, and Cybernetics, vol. 26, no. 1, pp 29–41, 1996.
[5] R.G. Reynolds, “An introduction to cultural algorithms,” in Proceedings of the 3rd Annual Conference on Evolutionary Programming, World Scientific Publishing, pp 131–139, 1994.
[6] R.G. Reynolds, “On modeling the evolution of Hunter-Gatherer decision-making systems,” in Geographical Analysis, vol. 10, no. 1, pp 31–46, 1978.
[7] C. Chung and R.G. Reynolds, “CAEP: An evolution-based tool for real-valued function optimization using cultural algorithms,” in International Journal on Artificial Intelligence Tools, vol. 7, no. 3, pp. 239–291, 1998.
[8] L.S Coelho, P. Alotto,“Electromagnetic Optimization Using a Cultural Self-Organizing Migrating Algorithm Approach Based on Normative Knowledge”, in IEEE Transactions on Magnetics, vol. 45, no. 3, pp 1446-1449, 2009.
[9] R.G. Reynolds, M. Ali, “computing with social fabric The Evolution of Social Intelligence within a Cultural Framework”,In IEEE computational intelligence magazine, 2008
[10] C.J. Lin, C.H. Chen, C.T. Lin,“A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications”, in IEEE Transactions on Systems, Man, and Cybernetics —part C, vol. 39, no. 1, pp 55-68,2009
[11] R. G. Reynolds, B. Peng, “Cultural Algorithms: Modeling of How Cultures Learn to Solve Problems” ,in Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2004
[12] R.G. Reynolds, “An adaptive computer model of plant collection and early agriculture in the eastern valley of Oaxaca,” in Guila Naquitz: Archaic Foraging and Early Agriculture in Oaxaca, Mexico, K. V. Flannery Ed, Academic Press, pp. 439–500.
[13] R.Iacoban, R. G. Reynolds, J. Brewste, “Cultural swarms: modeling the impact of culture on social interaction and problem solving,” Proc IEEE Swarm Intelligence Symposiu, Indianapolis,MI, USA, PP. 205-211,2003
[14] Zhi-Hui Zhan, Jun Zhang, Yun Li, and Henry Shu-Hung Chung, “Adaptive Particle Swarm Optimization,” IEEE Transaction on SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 39, NO. 6, DECEMBER 2009
[15] R.G. Reynolds and S.M. Saleem, “The impact of environmental dynamics on cultural emergence,” in Perspectives on Adaptions in Natural and Artificial Systems. Oxford University Press, pp. 253–280, 2001.
[16] X. Jin and R.G. Reynolds, “Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm approach,” in Proceeding of the 1999 Congress on Evolutionary Computation, pp. 1672–1678, 1999.
[17] J. Zhang, H. S.-H. Chung, and W.-L. Lo, “Clustering-based adaptive crossover and mutation probabilities for genetic algorithms,” IEEE Trans. Evol. Comput., vol. 11, no. 3, pp. 326–335, Jun. 2007.
[18] Z. H. Zhan, J. Xiao, J. Zhang, and W. N. Chen, “Adaptive control of acceleration coefficients for particle swarm optimization based on clustering analysis,” in Proc. IEEE Congr. Evol. Comput., Singapore, Sep. 2007, pp. 3276–3282.
[19] J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Englewood Cliffs, NJ: Prentice-Hall, 1997.
[20] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281–295, Jun. 2006.
[21] X. Yao, Y. Liu, and G. M. Lin, “Evolutionary programming made faster,” IEEE Trans. Evol. Comput., vol. 3, no. 2, pp. 82–102, Jul. 1999.
[22] N. Shafiabady, M. Teshnehlab and M. A. Nekoui “Some Applications of S.T. (Shafiabady-Teshnehlab) Evolutionary Optimization Algorithm”, International Journal of Innovative Computing, Information and Control (IJICIC), Vol 8, no 2, Feb 2012.
[23] G. Onwubolu and B. Babu, “New Optimization Techniques in Engineering, ” Berlin, Germany: Springer-Verlag, 2004.
[24] R. Eberhart, Y. Shi, and J. Kennedy, “Swarm Intelligence, ” San Mateo,CA: Morgan Kaufmann, 2001.
[25] Alireza Alfi ,“Particle Swarm Optimization Algorithm with Dynamic Inertia Weight for Online Parameter Identification Applied to Lorenz Chaotic System” ”, International Journal of Innovative Computing, Information and Control (IJICIC), Vol 8, no 2, Feb 2012.
[26] T. Back, “Evolutionary Algorithms in Theory and Practice,” Oxford,U.K.: Oxford Univ. Press, 1996.
[27] Z. Michalewicz, “Genetic Algorithms +Data Structures = Evolution Programs,” New York: Springer, 1992.
[28] D. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine,” Learning. Reading, MA: Addison-Wesley, 1989.