Associate Professor Tsung-Che Chiang

Department of Computer Science and Information Engineering
National Taiwan Normal University

Tel: +886-2-77346692    Fax: +886-2-29322378    Email

Tsung-Che Chiang, Chen-Nan Chen, and Yu-Chieh Lin, Parameter control mechanisms in differential evolution: a tutorial review and taxonomy, IEEE Symposium Series on Computational Intelligence, Singapore, April 2013.  (Presentation slides)

Full text: Official version; Unedited version

Abstract

Differential evolution (DE) is a promising algorithm for continuous optimization. Its two parameters, CR and F, have great effect on the algorithm performance. In recent years many DE algorithms with parameter control mechanisms were proposed. In this paper we propose a taxonomy to classify these algorithms according to the number of candidate parameter values, the number of parameter values used in a single generation, and the source of considered information. We classify twenty-three recent studies into nine categories and review their design features. Two types of relationships between these algorithms and several research directions are also summarized..

Taxonomy : x / y / z 

x (the number of candidate parameter values): con (continuous), dis (discrete)

y (the number of parameter values used in a single generation): 1, mul (multiple), idv (individual), var (variable), obj (objective), spp (sub-population)

z (information for updating parameter values): rnd (random), pop (population), par (parent), idv (individual), prb (problem)

References

con/1/pop >>

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J. Liu and J. Lampinen, “A fuzzy adaptive differential evolution algorithm,” Soft Computing, vol. 9, no. 6, pp. 448–462, 2005. [FADE]

W. Qian and A. Li, “Adaptive differential evolution algorithm for multiobjective optimization problems,” Applied Mathematics and Computation, vol. 201, no. 1-2, pp. 431–440, 2008. [ADEA] (MO)

con/1/prb >>

T. Takahama and S. Sakai, “Differential evolution with dynamic strategy and parameter selection by detecting landscape modality,” IEEE Congress on Evolutionary Computation, pp. 2114–2121, 2012. [LMDE]

T. Takahama and S. Sakai, “Large scale optimization by differential evolution with landscape modality detection and a diversity archive,” IEEE Congress on Evolutionary Computation, pp. 2842–2849, 2012. [LMDEa]

con/mul/rnd >>

Kaelo & Ali, “A numerical study of some modified differential evolution algorithms,” European Journal of Operational Research, 2006, pp. 1176–1184. [DERL/DELB]

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X. Li and M. Yin, “Modified differential evolution with self-adaptive parameters method,” Journal of Global Optimization, 2014. [MDE] (in terms of F control)

con/mul/pop >>

A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, 2009. [SaDE]

Z. Yang, K. Tang, and X. Yao, “Self-adaptive differential evolution with neighborhood search,” IEEE Congress on Evolutionary Computation, pp. 1110–1116, 2008. [SaNSDE]

J. Zhang and A. C. Sanderson, “JADE: adaptive differential evolution with optional external archive,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 945–958, 2009. [JADE]

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Z. Yang, K. Tang, and X. Yao, “Scalability of generalized adaptive differential evolution for large-scale continuous optimization,” Soft Computing, vol. 15, pp. 2141–2155, 2011. [GaDE]

S. M. Islam, S. Das, S. Ghosh, S. Roy, and P. N. Suganthan, “An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization,” IEEE Transactions on Systems, Man, and Cybernetics – Part B, vol. 42, no. 2, pp. 482–500, 2012. [MDE_pBX]

W. Gong, Z. Cai, and Y. Wang, “Repairing the crossover rate in adaptive differential evolution,” Applied Soft Computing, vol. 15, pp. 149–168, 2014. [Rcr-JADE]

D. Darko Kovačević, N Mladnović, B. Petrović, and Pavle Milošević, “DE-VNS: Self-adaptive differential evolution with crossover neighborhood search for continuous global optimization,” Computers & Operations Research, vol. 52, pp. 157–169, 2014. [DE-VNS] (in terms of CR control)

X. Li and M. Yin, “Modified differential evolution with self-adaptive parameters method,” Journal of Global Optimization, 2014. [MDE] (in terms of CR control)

con/idv/rnd >>

J. Brest, S. Greiner, B. Bošković, M. Mernik, and V. Žumer, “Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 646–657, 2006. [jDE]

J. Brest, V. Žumer, and M. S. Maučec, “Self-adaptive differential evolution algorithm in constrained real-parameter optimization,” IEEE Congress on Evolutionary Computation, pp. 215 –222, 2006. [jDE-2]

O. S. Soliman and L. T. Bui, “A self-adaptive strategy for controlling parameters in differential evolution,” IEEE Congress on Evolutionary Computation, pp. 2837–2842, 2008. [CSDE]

con/idv/par >>

H. A. Abbass, “The self-adaptive Pareto differential evolution algorithm,” IEEE Congress on Evolutionary Computation, pp. 831–836, 2002. [SPDE] (MO)

Y. Y. Fan and J. Lampinen, “A trigonometric mutation operation to differential evolution,” Journal of Global Optimization, pp. 105–129, 2003. [TDE]

M. G. H. Omran, A. Salman, and A. P. Engelbrecht, “Self-adaptive differential evolution,” Lecture Notes in Artificial Intelligence, vol. 3801, pp. 192–199, 2005. [SDE]

J. Teo, “Exploring dynamic self-adaptive populations in differential evolution,” Soft Computing, vol. 10, pp. 673–686, 2006. [DESAP]

A. Zamuda, J. Brest, B. Bošković, and V. Žumer, “Differential evolution for multiobjective optimization with self adaptation,” IEEE Congress on Evolutionary Computation, pp. 3617–3624, 2007. [DEMOwSA] (MO)

S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, “Differential evolution with a neighborhood-based mutation operator,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 526–553, 2009. [DEGL]

J. Brest and M. S. Maučec, “Self-adaptive differential evolution algorithm using population size reduction and three strategies,” Soft Computing, vol. 15, pp. 2157–2174, 2011. [jDElscop]

S. M. Elsayed, R. A. Sarker, and D. L. Essam, “An improved self-adaptive differential evolution algorithm for optimization problems,” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 89–98, 2013. [ISAMODE-CMA]

con/idv/pop >>

A. Nobakhti and H. Wang, “A simple self-adaptive differential evolution algorithm with application on the ALSTOM gasifier,” Applied Soft Computing, vol. 8, no. 1, pp. 350–370, 2008. [RADE]

L. Jia, W. Gong, and H. Wu, “An improved self-adaptive control parameter of differential evolution for global optimization,” In Z. Cai et al. (Eds.) Computational Intelligence and Intelligent Systems, 2009, pp. 215–224. [ISADE]

A. Ghosh, S. Das, A. Chowdhury, and R. Giri, “An improved differential evolution algorithm with fitness-based adaptation of the control parameters,” Information Sciences, vol. 181, pp. 3749–3765, 2011. [FiADE]

con/idv/idv >>

F. Neri and V. Tirronen, “Scale factor local search in differential evolution,” Memetic Computing, vol. 1, no. 2, pp. 153–171, 2009. [SFLSDE]

Y. N. Wang, L. H. Wu, and X. F. Yuan, “Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure,” Soft Computing, vol. 14, no. 3, pp. 193–209, 2010. [MOSADE] (MO)

Q. K. Pan, P. N. Suganthan, L. Wang, L. Gao, and R. Mallipeddi, “A differential evolution algorithm with self-adapting strategy and control parameters,” Computers & Operations Research, vol. 38, no. 1, pp. 394–408, 2011. [SspDE]

Y. Wang, H.-X. Li, T. Huang, and L. Li, “Differential evolution based on covariance matrix learning and bimodal distribution parameter setting,” Applied Soft Computing, vol. 18, pp. 232–247, 2014. [CoBiDE]

con/var/pop >>

D. Zaharie and D. Petcu, “Adaptive Pareto differential evolution and its parallelization,” Lecture Notes in Computer Science, vol. 3019, pp. 261–268, 2004. [APDE] (MO)

con/obj/pop >>

V. L. Huang, S. Z. Zhao, R. Mallipeddi, and P. N. Suganthan, “Multi-objective optimization using self-adaptive differential evolution algorithm,” IEEE Congress on Evolutionary Computation, pp. 190–194, 2009. [OW-MOSaDE] (MO)

con/spp/rnd >>

M. Weber, F. Neri, and V. Tirronen, “A study on scale factor in distributed differential evolution,” Information Sciences, pp. 2488–2511, 2011.

con/spp/pop >>

M. Weber, V. Tirronen, and F. Neri, “Scale factor inheritance mechanism in distributed differential evolution,” Soft Computing, pp. 1187–1207, 2010. [FACPDE]

dis/mul/rnd >>

Y. Wang, Z. Cai, and Q. Zhang “Differential evolution with composite trial vector generation strategies and control parameters,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 55–66, 2011. [CoDE]

dis/mul/pop >>

J. Tvrdik, “Adaptation in differential evolution: a numerical comparison,” Applied Soft Computing, vol. 9, no. 3, pp. 1149–1155, 2009. [DEBR]

D. Darko Kovačević, N Mladnović, B. Petrović, and Pavle Milošević, “DE-VNS: Self-adaptive differential evolution with crossover neighborhood search for continuous global optimization,” Computers & Operations Research, vol. 52, pp. 157–169, 2014. [DE-VNS] (in terms of F control)

R. A. Sarker, S. M. Elsayed, and T. Ray, ”Differential evolution with dynamic parameters selection for optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 5, pp. 689–707, 2014. [DE-DPS]

dis/idv/pop >>

R. Mallipeddi, P. N. Suganthan, Q. K. Pan, and M. F. Tasgetiren, “Differential evolution algorithm with ensemble of parameters and mutation strategies,” Applied Soft Computing, vol. 11, pp. 1679–1696, 2011. [EPSDE]
　

Any research result related to parameter control in differential evolution is very welcome. I am willing to update this list. Please email your new results to me.