Abstract
Differential evolution (DE) is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to balance the exploration and exploitation is harder work to the researchers improving the performance of DE. Very often, we catch one and lose another. To overcome this problem, this paper presents a novel DE variant, called heterozygous DE with Taguchi local search (THDE), in which two new proposed methods (i.e., multiple schemes heterozygous evolution and Taguchi local search) are employed, with one as enhanced exploration ability and the other enhanced exploitation ability. The experimental studies have been conducted on 27 well-known test functions, including unimodal, multimodal and shifted test functions. Experimental results have verified the quality and effectiveness of THDE. Comparison with the state-of-the-art DE variants has proved that THDE is a type of new competitive algorithm.
Similar content being viewed by others
References
Ali M, Pant M (2011) Improving the performance of differential evolution algorithm using cauchy mutation. Soft Comput 15(5):991–1007
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 Trans Evol Comput 10(6):646–657
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553
De Falco I, Della Cioppa A, Maisto D, Tarantino E (2008) Differential evolution as a viable tool for satellite image registration. Appl Soft Comput 8(4):1453–1462
Dorronsoro B, Bouvry P (2011) Improving classical and decentralized differential evolution with new mutation operator and population topologies. IEEE Trans Evol Comput 15(1):67–98
Du JX, Huang DS, Wang XF, Gu X (2007) Shape recognition based on neural networks trained by differential evolution algorithm. Neurocomputing 70(4):896–903
Fan HY, Lampinen J (2003) A trigonometric mutation operation to differential evolution. J Glob Optim 27(1):105–129
Fang K, Wang Y (1994) Number-theoretic methods in statistics. Chapmman & Hall, London
Gong W, Cai Z, Jiang L (2008) Enhancing the performance of differential evolution using orthogonal design method. Appl Math Comput 206(1):56–69
Gong W, Cai Z, Ling CX (2010) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15(4):645–665
Ho WH, Chan ALF (2010) Hybrid Taguchi-differential evolution algorithm for parameter estimation of differential equation models with application to hiv dynamics. Math Probl Eng
Ilonen J, Kamarainen JK, Lampinen J (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17(1):93–105
Jia G, Wang Y, Cai Z, Jin Y (2013) An improved (\(\mu +\lambda \))-constrained differential evolution for constrained optimization. Inf Sci 222:302–322
Leung YW, Wang Y (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans Evol Comput 5(1):41–53
Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462
Lopes HS, Bitello R (2007) A differential evolution approach for protein folding using a lattice model. J Comput Sci Technol 22(6):904–908
Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696
Mininno E, Neri F, Cupertino F, Naso D (2011) Compact differential evolution. IEEE Trans Evol Comput 15(1):32–54
Neri F, Tirronen V (2009) Scale factor local search in differential evolution. Memet Comput 1(2):153–171
Noman N, Iba H (2007) Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 4(4):634–647
Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1):107–125
Omidvar MN, Li X, Mei Y, Yao X (2014) Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans Evol Comput 18(3):378–393
Omran MG, Engelbrecht AP, Salman A (2009) Bare bones differential evolution. Eur J Oper Res 196(1):128–139
Paterlini S, Krink T (2006) Differential evolution and particle swarm optimisation in partitional clustering. Comput Stat Data Anal 50(5):1220–1247
Phadke MS (1995) Quality engineering using robust design. Prentice Hall PTR, London
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79
Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. In: Technical report TR-95-012. International Computer Science Institute, Berkeley
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. In: KanGAL, report 2005005
Sun J, Zhang Q, Tsang EP (2005) DE/EDA: a new evolutionary algorithm for global optimization. Inf Sci 169(3):249–262
Tasgetiren MF, Suganthan P (2006) A multi-populated differential evolution algorithm for solving constrained optimization problem. In: IEEE congress on evolutionary computation, CEC 2006, IEEE, pp 33–40
Tsai JT, Liu TK, Chou JH (2004) Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Trans Evol Comput 8(4):365–377
Tsai JT, Ho WH, Chou JH, Guo CY (2011) Optimal approximation of linear systems using Taguchi-sliding-based differential evolution algorithm. Appl Soft Comput 11(2):2007–2016
Wang Y, Cai Z (2012) Combining multiobjective optimization with differential evolution to solve constrained optimization problems. IEEE Trans Evol Comput 16(1):117–134
Wang Y, Li B, Weise T (2010) Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems. Inf Sci 180(12):2405–2420
Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66
Wang Y, Cai Z, Zhang Q (2012) Enhancing the search ability of differential evolution through orthogonal crossover. Inf Sci 185(1):153–177
Wang H, Rahnamayan S, Sun H, Omran MG (2013) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43(2):634–647
Wang Y, Li HX, Huang T, Li L,(2014) Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl Soft Comput 18(5):232–247
Xie W, Yu W, Zou X (2012) Diversity-maintained differential evolution embedded with gradient-based local search. Soft Comput 1–25
Yang GY, Dong ZY, Wong KP (2008a) A modified differential evolution algorithm with fitness sharing for power system planning. IEEE Trans Power Syst 23(2):514–522
Yang Z, Tang K, Yao X (2008b) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102
Yildiz AR (2013a) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433–1439
Yildiz AR (2013b) A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Appl Soft Comput 13(3):1561–1566
Zhang J, Sanderson AC (2009) Jade: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Zhu W, Tang Y, Fang J, Zhang W (2012) Adaptive population tuning scheme for differential evolution. Inf Sci
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61070008, No. 61364025), the Foundation of State Key Laboratory of Software Engineering (No. SKLSE2012-09-39), and the Science and Technology Foundation of Jiangxi Province, China (No. GJJ13729) as well.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Peng, H., Wu, Z. Heterozygous differential evolution with Taguchi local search. Soft Comput 19, 3273–3291 (2015). https://doi.org/10.1007/s00500-014-1482-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-014-1482-7