Abstract
Differential evolution (DE) algorithm is a well-known and straightforward population-based optimization approach to deal with nonlinear and composite problems. The scale factor (F) and crossover rate (CR) are two control parameters which play a crucial role to keep up the proper equilibrium between exploration and exploitation processes. The perturbation in the new solutions is controlled by CR, and the step size is managed by F during the solution search process. The step size of an individual is tuned to explore or exploit the search region of the solving problem. Large step size is used to explore while small step size is used to exploit the search region. Therefore, a fine-tuned step size can avoid the situation of skipping the true optima while maintaining the proper convergence speed. Researchers are working hard to adjust the step size as per the search progress. Therefore, this paper presents descriptive details of DE and a review on the various scale factor strategies in DE with their comparative impact on the solution search process.
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References
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)
Choudhary, N., Sharma, H., Sharma, N.: Differential evolution algorithm using stochastic mutation. In: 2016 International Conference on Computing, Communication and Automation (ICCCA), pp. 315–320. IEEE (2016)
Cui, L., Li, G., Lin, Q., Chen, J., Nan, L.: Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput. Oper. Res. 67, 155–173 (2016)
Das, S., Konar, A., Chakraborty, U.K.: Two improved differential evolution schemes for faster global search. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 991–998. ACM (2005)
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)
Dragoi, E.N., Dafinescu, V.: Parameter control and hybridization techniques in differential evolution: a survey. Artif. Intell. Rev. 45(4), 447–470 (2016)
Eigen, M.: Ingo Rechenberg Evolutionsstrategie Optimierung technischer Systeme nach Prinzipien der biologishen Evolution. mit einem Nachwort von Manfred Eigen, Friedrich Frommann Verlag, Struttgart-Bad Cannstatt (1973)
Engelbrecht, A.P.: Computational Intelligence: An Introduction. Wiley (2007)
Fan, Q., Yan, X., Xue, Y,: Prior knowledge guided differential evolution. Soft Comput. 1–18 (2016)
Islam, S.M., Das, S., Ghosh, S., Roy, S., Suganthan, P.N.: An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans. Syst. Man Cybern., Part B (Cybernetics) 42(2):482–500 (2012)
Kiefer, J.: Sequential minimax search for a maximum. Proc. Am. Math. Soc. 4(3), 502–506 (1953)
Li, X., Yin, M.: Modified differential evolution with self-adaptive parameters method. J. Comb. Optim. 31(2), 546–576 (2016)
Neri, F., Tirronen, V.: Scale factor local search in differential evolution. Memetic Comput. 1(2), 153–171 (2009)
Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)
Price, K.V.: Differential evolution: a fast and simple numerical optimizer. In: 1996 Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS, pp. 524–527. IEEE (1996)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Egnlewood Cliffs, 25–27 (1995)
Sharma, H., Bansal, J.C., Arya, K.V.: Dynamic scaling factor based differential evolution algorithm. In: Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), pp. 73–85. Springer (2012), 20–22 Dec 2011
Sharma, H., Bansal, J.C., Arya, K.V.: Self balanced differential evolution. J. Comput. Sci. 5(2), 312–323 (2014)
Sharma, H., Shrivastava, P., Bansal, J.C., Tiwari, R.: Fitness based self adaptive differential evolution. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2013), pp. 71–84. Springer (2014)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Viswanathan, G.M., Afanasyev, V., Buldyrev, S.V., Murphy, E.J., et al.: Lévy flight search patterns of wandering albatrosses. Nature 381(6581), 413 (1996)
Wang, Y., Cai, Zixing, Zhang, Qingfu: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)
Zhang, J., Sanderson, A.C.: Jade: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
Zhao, Z., Yang, J., Ziyu, H., Che, H.: A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric latin hypercube design for unconstrained optimization problems. Eur. J. Oper. Res. 250(1), 30–45 (2016)
Zou, D.X., Pan, G., Qi, H.W., Li, Y.P.: An effective modified differential evolution algorithm for reliability problems. In: 2016 4th International Symposium on Computational and Business Intelligence (ISCBI), pp. 132–136. IEEE (2016)
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Sharma, P., Sharma, H., Kumar, S., Bansal, J.C. (2019). A Review on Scale Factor Strategies in Differential Evolution Algorithm. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_73
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