Abstract
Differential evolution (DE) has been extensively used in optimization problem. However, original DE has some shortcomings. Up to now, there have been a lot of its variations. In this paper, a modified version of differential evolution algorithm is raised on the basis of clustering-based differential evolution with random-based sampling and Gaussian sampling. The modified one is called MGRCDE. It can enhance the ability of searching for final solution with better quality by maintaining the diversity of population and local search around individuals with the best quality in the subpopulation. At the same time, it accelerates convergence rate of evolution process by clustering. Twenty-five standard, unconstrained single-objective benchmark functions have been used in verifying the performance of the modified algorithm, and a comparison between the modified algorithm and the previous one has been made. The results demonstrate that the modified algorithm can control the population to move toward global optimal point more effectively, having a better ability of global optimization. Especially in high-dimensional functions, the advantage has been proved more obvious.
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Abbass HA (2002) The self-adaptive pareto differential evolution algorithm. IEEE C Evol Comput 1:831–836
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE T Evolut Comput 10:646–657
Brest J, Maucec MS (2011) Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft Comput 15:2157–2174. https://doi.org/10.1007/s00500-010-0644-5
Elsayed SM, Sarker RA, Essam DL (2013a) An improved self-adaptive differential evolution algorithm for optimization problems. IEEE Trans Ind Inform 9:89–99. https://doi.org/10.1109/Tii.2012.2198658
Elsayed SM, Sarker RA, Essam DL (2013b) Self-adaptive differential evolution incorporating a heuristic mixing of operators. Comput Optim Appl 54:771–790. https://doi.org/10.1007/s10589-012-9493-8
García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez AM (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185:1088–1113
Gong W, Cai Z, Jiang L (2008) Enhancing the performance of differential evolution using orthogonal design method. Appl Math Comput 206:56–69
Huang VL, Qin AK, Suganthan PN (2006) Self-adaptive differential evolution algorithm for constrained real-parameter optimization. In: IEEE congress on evolutionary computation, CEC 2006, pp 17–24
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323
Joshi R, Sanderson AC (1997) Minimal representation multisensor fusion using differential evolution. In: IEEE international symposium on computational intelligence in robotics and automation, p 266
Lee CH, Kuo CT, Chang HH (2012) Performance enhancement of the differential evolution algorithm using local search and a self-adaptive scaling factor. Int J Innov Comput I(8):2665–2679
Liu G, Guo Z (2016) A clustering-based differential evolution with random-based sampling and Gaussian sampling. Elsevier, Amsterdam
Liu G, Li Y, Nie X, Zheng H (2012) A novel clustering-based differential evolution with 2 multi-parent crossovers for global optimization. Appl Soft Comput 12:663–681
Liu G, Xiong C, Guo Z (2015) Enhanced differential evolution using random-based sampling and neighborhood mutation. Soft Comput 19:2173–2192
Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9:448–462
Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11:1679–1696
Ogiela L (2008) Cognitive computational intelligence in medical pattern semantic understanding. In: International conference on natural computation, pp 245–247
Ogiela L (2015) Cryptographic techniques of strategic data splitting and secure information management. Pervasive Mobile Comput 29:130–141
Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Natural computing series. Springer, New York
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13:398–417. https://doi.org/10.1109/Tevc.2008.927706
Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: The 2005 IEEE congress on evolutionary computation 2005, vol 1782, pp 1785–1791
Rahnamayan S, Wang GG (2009) Center-based sampling for population-based algorithms. In: IEEE congress on evolutionary computation 2009 (CEC ’09), pp 933–938
Sethi PC, Behera PK (2015) Secured packet inspection with hierarchical pattern matching implemented using incremental clustering algorithm. In: International conference on high performance computing and applications, pp 1–6
Storn R (1996) On the usage of differential evolution for function optimization. In: Biennial conference of the North American fuzzy information processing society (NAFIPS), IEEE, Berkeley, pp 519–523
Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical, Report TR-95-012, ICSI
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/a:1008202821328
Teo J (2006) Exploring dynamic self-adaptive populations in differential evolution. Soft Comput 10:673–686
Ugolotti R, Nashed YSG, Mesejo P, Ivekovič Š, Mussi L, Cagnoni S (2013) Particle Swarm Optimization and Differential Evolution for model-based object detection. Appl Soft Comput J 13:3092–3105
Vesterstrom J, Thomsen RA (2013) comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on evolutionary computation, 2004. (CEC ’04), vol 1982, pp 1980–1987
Wang H, Rahnamayan S, Sun H, Omran MGH (2013) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43:634
Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evolut Comput 15:55–66
Xue F, Sanderson AC, Bonissone PP, Graves RJ (2005) Fuzzy logic controlled multi-objective differential evolution. In: The IEEE international conference on fuzzy systems, pp 720–725
IEEE Trans Evolut Comput (2002) Evolutionary programming made faster—evolutionary computation. 3:82–102
Zhang J, Avasarala V, Sanderson AC, Mullen T (2008) Differential evolution for discrete optimization: an experimental study on combinatorial auction problems. In: Evolutionary computation, pp 2794–2800
Zhang J, Avasarala V, Subbu R (2010) Evolutionary optimization of transition probability matrices for credit decision-making. Eur J Oper Res 200:557–567
Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13:945–958
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This study was funded by the National Science and technology support program under Grant 2015BAF11B01 and the Hunan Natural Science Fund Project Grant 14JJ1011.
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Sun, W., Song, Y., Lin, A. et al. Modified clustering-based differential evolution with a flexible combination of exploration and exploitation. Soft Comput 22, 6087–6098 (2018). https://doi.org/10.1007/s00500-017-2950-7
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DOI: https://doi.org/10.1007/s00500-017-2950-7