Abstract
This paper proposes a novel evolutionary computation (EC) algorithm, fuzzy scaled mutation evolutionary computation (FSMEC), for solving nonlinear numerical optimization problems. Although EC has been typically used for obtaining nonlinear optimal solutions for several years, users are required to determine the parameters of the algorithm. In this study, a fuzzy inference system (FIS) was used to determine the mutation factor of the FSMEC algorithm according to the change in the solution and the distance between the whole best and each individual. The experimental results revealed that the FIS operates effectively. CEC2013 numerical optimization problems without rotation and shift were used as test functions. The FSMEC algorithm determined optimal solutions in 10, 30, and 50 dimensions for all unimodal functions. The convergence generations were less than 100 in 10 dimensions. The FSMEC algorithm obtains 16, 11, and 10 optimal values for 28 functions in 10, 30, and 50 dimensions, respectively. Moreover, statistical hypothesis tests demonstrated that the performance of the FSMEC algorithm in deriving optimal solutions was 68 %, which was higher than those of the FADE and SMEC algorithms for 28 test functions.
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References
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceeding of the IEEE International Joint Conference on Neural Networks, 1942–1948 (1995)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: IEEE Congress on Evolutionary Computing, Anchorage, AK, May, pp. 4–9 (1998)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Storn, R., Price, K.: Differential evolution- a simple and efficient heuristic for global optimization over continuous space. J. Glob. Optim. 11, 341–359 (1997)
Zhan, Z.H., Zhang, J., Li, Y., Chung, H.S.H.: Adaptive particle swarm optimization. In: IEEE Transaction on System, Man, and Cybernetics—Part B: Cybernetics, vol. 39, (no. 6), pp. 1362–1381 (2009)
Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. Soft. Comput. 9(6), 448–462 (2005)
Wang, J.Z., Wu, D.F., Sun, T.Y.: An improved strategy based on center of solutions for differential evolution algorithm. In: IEEE Congress on Evolutionary Computing, Sendai, Japan, May, pp. 24–28 (2015)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. In: IEEE Transaction on System, Man, and Cybernetics, vol. 15, (no. 1), pp. 116–132 (1985)
Liang, J.J., Qu, B.Y., Suganthan, P.N., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical Report 201212. Computational Intelligence Labotatory, Zhengzhou University, Zhenazhou China and Technical Report, Nanyang Technological University, Singapore (2013)
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This work was supported by the National Science Council of Taiwan through the Ministry of Science and Technology under the Grant no. NSC 101-2221-E-259-008-MY3 and Grant No. MOST 104-2221-E-259-033-MY3.
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Wang, JZ., Ho, Y. & Sun, TY. Fuzzy Scaled Mutation Evolutionary Computation. Int. J. Fuzzy Syst. 18, 1162–1179 (2016). https://doi.org/10.1007/s40815-016-0155-3
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DOI: https://doi.org/10.1007/s40815-016-0155-3