On the adaptation of the mutation scale factor in differential evolution

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• 作者：Carlos Segura (1)
  Carlos A. Coello Coello (2)
  Eduardo Segredo (3)
  Coromoto Le贸n (3)
  
  1. Departamento de Computaci贸n ; CINVESTAV-IPN ; UMI LAFMIA 3175 CNRS ; Av. IPN No. 2508 ; Col. San Pedro Zacatenco ; 07300聽 ; M茅xico ; DF ; M茅xico
  2. Departamento de Computaci贸n (Evolutionary Computation Group) ; CINVESTAV-IPN ; Av. IPN No. 2508 ; Col. San Pedro Zacatenco ; 07300聽 ; M茅xico ; DF ; M茅xico
  3. Dpto. Estad铆stica ; I.O. y Computaci贸n ; Astrof铆sico Fco. S谩nchez ; Edif. Matem谩ticas ; Universidad de La Laguna ; 38271聽 ; Santa Cruz de Tenerife ; Spain
  
• 关键词：Differential evolution ; Mutation scale factor ; Adaptation ; Parameter control
• 刊名：Optimization Letters
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：9
• 期：1
• 页码：189-198
• 全文大小：195 KB
• 参考文献：1. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. Trans. Evol. Comput. 10(6), 646鈥?57 (2006)
  2. Das, S., Suganthan, P.: Differential evolution: a survey of the state-of-the-art 15(1), 4鈥?1 (2011)
  3. LaTorre, A., Muelas, S., Pea, J.M.: A mos-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test. Soft Comput. 15(11), 2187鈥?199 (2011) CrossRef
  4. Lozano, M., Molina, D., Herrera, F.: Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Comput. 15(11):2085-2087 (2011)
  5. Mallipeddi, R., Suganthan, P., Pan, Q., Tasgetiren, M.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679鈥?696 (2011) CrossRef
  6. Mezura-Montes, E., Vel谩zquez-Reyes, J., Coello Coello, C.A. : A comparative study of differential evolution variants for global optimization. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation (GECCO鈥?6), pp. 485鈥?92. ACM, New York (2006)
  7. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1鈥?), 61鈥?06 (2010) 9-9137-2" target="_blank" title="It opens in new window">CrossRef
  8. Price, K.: Differential evolution. In: Zelinka, I., Sn谩s虇el, V., Abraham, A. (eds) Handbook of Optimization, Intelligent Systems Reference Library, vol. 38, pp. 187鈥?14. Springer, Berlin Heidelberg (2013)
  9. Price, K., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Government Printing Office, Natural Computing Series. USA (2005)
  10. Qin, A.K., Huang, V.L., Suganthan, P.: Differential evolution algorithm with strategy adaptation for global numerical optimization. Trans. Evol. Comput. 13(2), 398鈥?17 (2009)
  11. Soliman, O.S., Bui, L.T., Abbass, H.A.: The effect of a stochastic step length on the performance of the differential evolution algorithm. In: 2007 IEEE Congress on Evolutionary Computation (CEC鈥?7), pp. 2850鈥?857 (2007)
  12. Storn, R., Price, K.: Differential evolution鈥攁 simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341鈥?59 (1997) CrossRef
  13. Tvrd铆k, J., Pol谩kov谩, R., Veselsk媒, J., Bujok, P.: Adaptive variants of differential evolution: Towards control-parameter-free optimizers. In: Zelinka, I., Sn谩s虇el, V., Abraham, A. (eds) Handbook of Optimization, Intelligent Systems Reference Library, vol. 38, pp. 423鈥?49. Springer, Berlin Heidelberg (2013)
  14. Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. Trans. Evol. Comput. 15(1), 55鈥?6 (2011)
  15. Zhang, J., Sanderson, A.: JADE: Adaptive differential evolution with optional external archive. Trans. Evol. Comput. 13(5), 945鈥?58 (2009)
  16. Zielinski, K., Wang, X., Laur, R.: Comparison of adaptive approaches for differential evolution. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds) Parallel Problem Solving from Nature (PPSN X). Lecture Notes in Computer Science, vol. 5199, pp. 641鈥?50. Springer, Berlin Heidelberg (2008)
  17. Zielinski, K., Weitkemper, P., Laur, R., Kammeyer, K.D.: Parameter study for differential evolution using a power allocation problem including interference cancellation. In: IEEE Congress on Evolutionary Computation 2006 (CEC鈥?6), pp. 1857鈥?864 (2006)
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Optimization
  Operation Research and Decision Theory
  Numerical and Computational Methods in Engineering
  Numerical and Computational Methods
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1862-4480

文摘

Differential evolution (DE) is a simple yet effective metaheuristic specially suited for real-parameter optimization. The most advanced DE variants take into account the feedback obtained in the self-optimization process to modify their internal parameters and components dynamically. In recent years, some controversies have arisen regarding the adaptive schemes that incorporate feedback from the search process to guide the adaptation of the mutation scale factor. Some researchers have claimed that no significant benefits are obtained with these kinds of schemes. However, other studies have shown that they are highly effective. In this paper, we show that there is a relationship between the effectiveness of these adaptive schemes and the balance between exploration and exploitation induced by the trial vector generation strategy considered. State-of-the-art adaptive schemes are not useful for the trial vector generation strategies with the highest levels of exploration, which in fact seems to be the reason behind the controversies of recent years.