Improvement of Genetic Algorithm Using a Fuzzy Control Combined with Coevolutionary Algorithm

Document Type : Persian Original Article

Author

Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

In order to achieve the best performance in genetic algorithm, proper determination of parameters is necessary. This paper addresses the intelligent determination of the crossover probability between two selected parents in each generation. Unlike most existing techniques that utilize the diversity characteristics of each generation to determine the crossover probability throughout the current generation, this paper defines some novel phenotype and genotype features, and develops a zero-order Takagi-Sugeno fuzzy controller to derive the proper crossover probability for each selected parent pair. As such, each pair has a unique probability parameter that results in the flexibility of the standard genetic algorithm depending on the region being searched and avoids premature convergence. In addition, in the proposed method, the consequent part of the fuzzy rules is not fixed but is generated through a coevolutionary process and simultaneously with the decision variables of the optimization problem. This enhances the efficiency of the proposed method. The simulation results on a set of optimization benchmarks show the performance of this method. Its effectiveness is also investigated by applying it to the complicated problem of terrain avoidance/terrain following fly.

Keywords


  [1]     H. Sun, Y. Ge, W. Liu, and Z. Liu, “Geometric optimization of two-stage thermoelectric generator using genetic algorithms and thermodynamic analysis,” Energy, vol. 171, pp. 37–48, Mar. 2019.
  [2]     Y. Jiang, P. Wu, J. Zeng, Y. Zhang, Y. Zhang, and S. Wang, “Multi-parameter and multi-objective optimisation of articulated monorail vehicle system dynamics using genetic algorithm,” Veh. Syst. Dyn., pp. 1–18, Jan. 2019.
  [3]     S. Suri and R. Vijay, “A Bi-objective Genetic Algorithm Optimization of Chaos-DNA Based Hybrid Approach,” J. Intell. Syst., vol. 28, no. 2, pp. 333–346, Apr. 2019.
  [4]     R. Pereira and L. Aelenei, “Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms,” Renew. Energy, vol. 137, pp. 157–166, Jul. 2019.
  [5]     A. Shrestha and A. Mahmood, “Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture,” J. Math., vol. 2016, pp. 1–10, 2016.
  [6]     K. A. De Jong and W. M. Spears, “An analysis of the interacting roles of population size and crossover in genetic algorithms,” 1991, pp. 38–47.
  [7]     F. Herrera, M. Lozano, and J. L. Verdegay, “Dynamic and heuristic fuzzy connectives-based crossover operators for controlling the diversity and convergence of real-coded genetic algorithms,” Int. J. Intell. Syst., vol. 11, no. 12, pp. 1013–1040, Dec. 1998.
  [8]     J. Hesser and R. Männer, “Towards an optimal mutation probability for genetic algorithms,” in Parallel Problem Solving from Nature, Berlin/Heidelberg: Springer-Verlag, pp. 23–32.
  [9]     F. Herrera, M. Lozano, and J. L. Verdegay, “Fuzzy connectives based crossover operators to model genetic algorithms population diversity,” Fuzzy Sets Syst., vol. 92, no. 1, pp. 21–30, Nov. 1997.
[10]     J. E. Smith, “Self-Adaptation in Evolutionary Algorithms for Combinatorial Optimisation,” in Adaptive and Multilevel Metaheuristics. Studies in Computational Intelligence, Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 31–57.
[11]     E. Rodriguez-Tello and J. Torres-Jimenez, “Improving the Performance of a Genetic Algorithm Using a Variable-Reordering Algorithm,” 2004, pp. 102–113.
[12]     X. Yan, “An Improved Genetic Algorithm and Its Application in Classification,” Int. J. Comput. Sci. Issues, vol. 10, no. 1, pp. 337–346, 2013.
[13]     V. R. Kalatjari and M. H. Talebpour, “Optimization of Skeletal Structures Using Improved Genetic Algorithms Based on Proposed Sampling Search Space Idea,” Iran Univ. Sci. Technol., vol. 8, no. 3, pp. 415–432, Oct. 2018.
[14]     K. A. De Jong and W. M. Spears, “A formal analysis of the role of multi-point crossover in genetic algorithms,” Ann. Math. Artif. Intell., vol. 5, no. 1, pp. 1–26, Mar. 1992.
[15]     K. Li, A. Fialho, S. Kwong, and Q. Zhang, “Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition,” IEEE Trans. Evol. Comput., vol. 18, no. 1, pp. 114–130, Feb. 2014.
[16]     M. Jalali Varnamkhasti, L. S. Lee, M. R. Abu Bakar, and W. J. Leong, “A Genetic Algorithm with Fuzzy Crossover Operator and Probability,” Adv. Oper. Res., vol. 2012, pp. 1–16, 2012.
[17]     D. E. Goldberg and K. Sastry, “A Practical Schema Theorem for Genetic Algorithm Design and Tuning,” in Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, 2001, pp. 328–335.
[18]     H. Guo, Y. Feng, F. Hao, S. Zhong, and S. Li, “Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities,” J. Comput., vol. 9, no. 1, Jan. 2014.
[19]     H. C. . Lau, D. Nakandala, and L. Zhao, “Development of a hybrid fuzzy genetic algorithm model for solving transportation scheduling problem,” J. Inf. Syst. Technol. Manag., vol. 12, no. 3, Jan. 2016.
[20]     E. Khmeleva, A. A. Hopgood, L. Tipi, and M. Shahidan, “Fuzzy-Logic Controlled Genetic Algorithm for the Rail-Freight Crew-Scheduling Problem,” KI - Künstliche Intelligenz, vol. 32, no. 1, pp. 61–75, Feb. 2018.
[21]     L. Yao, Y. L. Jiang, and J. Xiao, “An Improved Fuzzy Adaptive Genetic Algorithm for Function Optimization,” Adv. Mater. Res., vol. 403–408, pp. 2598–2601, Nov. 2011.
[22]     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.
[23]     M. Yan et al., “Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow,” Meas. Sci. Technol., vol. 29, no. 5, p. 055404, May 2018.
[24]     A. Aleti and I. Moser, “A Systematic Literature Review of Adaptive Parameter Control Methods for Evolutionary Algorithms,” ACM Comput. Surv., vol. 49, no. 3, pp. 1–35, Oct. 2016.
[25]     F. Herrera and M. Lozano, “Fuzzy Genetic Algorithms: Issues and Models,” 1999.
[26]     L. J. Eshelman, R. A. Caruana, and J. D. Schaffer, “Biases in the Crossover Landscape,” in Proceedings of the Third International Conference on Genetic Algorithms, 1989, pp. 10–19.
[27]     م. قلعه نوئی, "بهبود الگوریتم ژنتیک با استفاده از ترکیب منطق فازی و الگوریتم هم‌تکاملی و کاربرد آن در مسأله پرواز تعقیب عوارض زمین," چهارمین کنفرانس تکنولوژی در مهندسی برق و کامپیوتر, 1398.
[28]     H. Wang, J. Liu, J. Zhi, and C. Fu, “The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization,” Math. Probl. Eng., vol. 2013, pp. 1–10, 2013.
[29]     O. Montiel, Y. Rubio, C. Olvera, and A. Rivera, “Quantum-Inspired Acromyrmex Evolutionary Algorithm,” Sci. Rep., vol. 9, no. 1, p. 12181, Dec. 2019.
[30]     C.-W. Lee and B.-Y. Lin, “Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting,” Energies, vol. 10, no. 11, p. 1832, Nov. 2017.
[31]     R. Lahoz-Beltra, “Quantum Genetic Algorithms for Computer Scientists,” Computers, vol. 5, no. 4, p. 24, Oct. 2016.
[32]     F. X. Blasco Ferragud, “Control predictivo basado en modelos mediante técnicas de optimización heurística. Aplicación a procesos no lineales y multivariables.,” Universitat Politècnica de València, Valencia (Spain), 1999.
[33]     D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1st ed. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 1989.
[34]     J. G. Digalakis and K. G. Margaritis, “On benchmarking functions for genetic algorithms,” Int. J. Comput. Math., vol. 77, no. 4, pp. 481–506, Jan. 2001.
[35]     W. Schwarting, J. Alonso-Mora, and D. Rus, “Planning and Decision-Making for Autonomous Vehicles,” Annu. Rev. Control. Robot. Auton. Syst., vol. 1, no. 1, pp. 187–210, May 2018.
[36]     Changwen Zheng, Lei Li, Fanjiang Xu, Fuchun Sun, and Mingyue Ding, “Evolutionary route planner for unmanned air vehicles,” IEEE Trans. Robot., vol. 21, no. 4, pp. 609–620, Aug. 2005.