Abstract
In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.
Similar content being viewed by others
References
Amir, H. M. and Hasegawa, T. (1989) Nonlinear mixed-discrete structural optimization. Journal of Structural Engineering, 115(3), 626-646.
Chen, J. L. and Tsao, Y. C. (1993) Optimal design of machine elements using genetic algorithms. Journal of the Chinese Society of Mechanical Engineers, 14(4), 193-199.
Cheong, F. and Lai, R. (2000) Constraining the optimization of a fuzzy logic controller using an enhanced genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics\3-Part B: Cybernetics, 30(1), 31-46.
Davis, L. (1991) Handbook of Genetic Algorithms, Van Nostrand Reinhold.
Gabriete, D. and Ragsdell, K. (1980) Large scale nonlinear programming using the generalized reduced gradient method. ASME Journal of Mechanical Design, 102, 566-573.
Gen, M. and Cheng, R. (1997) Genetic Algorithms and Engineering Design, John-Wiley & Sons.
Gen, M. and Cheng, R. (2000) Genetic Algorithms and Engineering Optimization, John-Wiley & Sons.
Himmelblau, M. (1972) Applied Nonlinear Programming, McGraw-Hill, New York.
Homaifer, A., Qi, C. and Lai, S. (1994) Constrained optimization via genetic algorithms. Simulation, 62(4), 242-254.
Ishibuchi, H., Yamamoto, N., Murata, T. and Tanaka, H. (1994) Genetic algorithm and neighborhood search algorithms for fuzzy flow-shop scheduling problems. Fuzzy Sets and Systems, 67, 81-100.
Lee, M. and Takagi, H. (1993) Dynamic control of genetic algorithm using fuzzy logic techniques. Proceedings of the 5th International Conference on Genetic Algorithms, San Francisco, pp. 76-83.
Li, B. and Jiang, W. (2000) A novel stochastic optimization algorithm. IEEE Transactions on Systems, Man, and Cybernetics\3-Part B: Cybernetics, 30(1), 193-198.
Mathias, K. E., Whitley, L. D., Stork, C. and Kusuma, T. (1994) Staged hybrid genetic search for seismic data imaging. Proceedings of the Evolutionary Computation, Orlando, FL, pp. 356-361.
Michalewicz, Z. (1994) Genetic Algorithms + Data Structures = Evolution Program, Second Extended Edition, Spring-Verlag.
Michalewicz, Z. and Janikow, C. Z. (1991) A handling constraint in genetic algorithms. Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, CA, pp. 151-157.
Renders, J. M. and Flasse, S. P. (1996) Hybrid methods using genetic algorithms for global optimization. IEEE Transactions on Systems, Man, and Cybernetics\3-Part B: Cybernetics, 26(2), 243-258.
Rogers, D. (1991) G/SPLINES: A hybrid of Friedman's multivariate adaptive regression splines (MARS) algorithm with Holland's genetic algorithm. Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, CA, pp. 384-391.
Sandgren, E. (1990) Nonlinear integer and discrete programming in mechanical design optimization. ASME Journal of Mechanical Design, 112(2), 223-229.
Souza, P. S. and Talukdar, S. N. (1991) Genetic algorithm in asynchronous teams. Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, CA, pp. 392-397.
Wang, P.T., Wang, G. S. and Hu, Z. G. (1997) Speeding up the search process of genetic algorithm by fuzzy logic. Proceeding of the 5th European Congress on Intelligent Techniques and Soft Computing, pp. 665-671.
Wu, S. J. and Chow, P. T. (1995) Genetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimization. Engineering Optimization, 24, 137-159.
Xu, H. and Vukovich, G. (1994) Fuzzy evolutionary algorithm and automatic robot trajectory generation. Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE Press, Piscataway, NJ, pp. 595-600.
Yen, J., Liao, J. C., Lee, B. J. and Randolph, D. (1998) A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method. IEEE Transactions on Systems, Man, and Cybernetics\3-Part B: Cybernetics, 28(2), 173-191.
Zeng, X. and Rabenasolo, B. (1997) A fuzzy logic based design for adaptive genetic algorithms. Proceedings of the 5th European Congress on Intelligent Techniques and Soft Computing, pp. 660-664.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Yun, Y., Gen, M. & Seo, S. Various hybrid methods based on genetic algorithm with fuzzy logic controller. Journal of Intelligent Manufacturing 14, 401–419 (2003). https://doi.org/10.1023/A:1024662112308
Issue Date:
DOI: https://doi.org/10.1023/A:1024662112308