Abstract
Past few decades have witnessed the growth and development of different optimization techniques that can be applied for solving complex problems that are otherwise difficult to solve by traditional methods. Differential evolution (DE) has attained the reputation of a powerful optimization technique that can be used for solving a wide range of problems. In DE, mutation is the most important operator as it helps in generating a new solution vector. In this paper we propose an additional mutation strategy for DE. The suggested strategy is named DE/rand-to-best-best/2. It makes use of an additional parameter called guiding force parameter K, which takes a value between (0,1) besides using the scaling factor F, which has a fixed value. DE/rand-to-best-best/2 makes use of two difference vectors, where the difference is taken from the best solution vector. One vector difference will be produced with a randomly generated mutation factor K (0,1). Advantage of this strategy is, it will add a different vector to the old one and search space will increase with a random factor. Result shows that this strategy performs well in comparison to other mutation strategies of DE.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Brest J, Boskovic B, Greiner S, Zumer V, Maucec MS (2007) Performance comparison of self-adaptive and adaptive differential evolution algorithms. Softw Comput 11(7):617–629
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006a) Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657
Brest J, Zumer V, Maucec MS (2006b) Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: Proceedings of IEEE 2003 Congress on Evolutionary Computation, Vancouver, BC pp 215–222
Das S, Abraham A, Chakraborthy UK (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(June):526–553
Deng W, Yang X, Zou L, Wang M, Liu Y, Li Y (2013) An improved self-adaptive differential evolution algorithm and its application. Chemometr Intell Lab Syst 128:66–76
Fan H-Y, Lampinen J (2003) A trigonometric mutation operation to differential evolution. J Glob Optim 27(1):105–129
Gamperle R, Muller SD, Koumoutsakos P (2002) A parameter study for differential evolution. In: Proceedings of the Advances in intelligent systems, fuzzy systems, evolutionary computation. Crete, pp 293–298
Gämperle R, Müller SD, Koumoutsakos P (2002) A parameter study for differentialevolution. In: Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, WSEAS Press, Interlaken, pp 293–298
Ghosh A et al (2011) An improved differential evolution algorithm with fitness-based adaptation of the control parameters. Inf Sci 181(18):3749–3765
Huang VL, Qin AK, Suganthan PN (2006) Self-adaptive differential evolution algorithm for constrained real-parameter optimization. In: Proceedings of IEEE 2003 Congress on Evolutionary Computation, pp 17–24
Iorio A, Li X (2004) Solving rotated multi-objective optimization problems using differential evolution. In: Australian Conference on Artificial Intelligence, Cairns, pp 861–872
Joshi R, Sanderson AC (1999) Minimal representation multisensory fusion using differential evolution. IEEE Trans Syst Man Cybern. Part A 29(1):63–76
Lampinen J, Zelinka I (2000) On stagnation of the differential evolution algorithm. In: Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing. pp 76–83
Mallipeddi R et al (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Softw Comput 11(2):1679–1696
Mezura-Montes E, Velázquez-Reyes J, Coello CA (2006) A comparative study of differential evolution variants for global optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, WA, pp 485–492
Pan Q-K et al (2011) A differential evolution algorithm with self-adapting strategy and control parameters. Comput Oper Res 38(1):394–408
Price KV (1999) An introduction to differential evolution. In: Corne D, Dorgio M, Glover F (eds) New ideas in optimization. McGraw-Hill, London, pp 79–108
Price KV, Storn RM, Lampinen JA (2005a) Differential evolution: a practical approach to global optimization, 1st edn. Springer, New York
Price KV, Storn RM, Lampinen JA (2005b) Differential evolution: a practical approach to global optimization, 1st edn. Springer, New York
Qin AK, Suganthan PN, (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedigs of the IEEE Congress Evolution Computation, vol 2, pp 1785–1791
Qin AK, Huang VL, Suganthan PN (2009a) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Qin AK, Huang VL, Suganthan PN (2009b) 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
Reed HM, Nichols JM, Earls CJ (2013) A modified differential evolution algorithm for damage identification in submerged shell structures. Mech Syst Signal Process 39:396–408
Sacco WF, Henderson N (2014) Differential evolution with topographical mutation applied to nuclear reactor core design. Progr Nucl Energy 70:140e–148
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 519–523
Storn R, Price K (1997a) Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Storn R, Price K (1997b) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359
Zhang J, Avasarala V, Subbu R (2010) Evolutionary optimization of transition probability matrices for credit decision-making. Eur J Oper Res 200(2):557–567
Zhang J, Avasarala V, Sanderson AC, Mullen T (2008) Differential evolution for discrete optimization: An experimental study on combinatorial auction problems. In: Proceedings of the IEEE World Congress Computation Intelligent, Hong Kong, pp 2794–2800
Zhang J, Sanderson AC (2007) An approximate Gaussian model of differential evolution with spherical fitness functions. In: Proceedings of the IEEE Congress Evolution Computation, Singapore, pp 2220–2228
Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Zhang WJ, Xie XF (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: Proceedings of the IEEE International Conference Systems Man Cybernetics, Washington, DC, pp 3816–3821
Zou D, Wu J, Gao L, Li S (2013) A modified differential evolution algorithm for unconstrained optimization problems. Neurocomputing 120:469–481
Acknowledgments
The reported study was partially supported by DST, research project No. INT/RFBR/P-164, and RFBR, research project No. 14-01-92694 IND-a
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zaheer, H., Pant, M., Kumar, S. et al. A new guiding force strategy for differential evolution. Int J Syst Assur Eng Manag 8 (Suppl 4), 2170–2183 (2017). https://doi.org/10.1007/s13198-014-0322-6
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-014-0322-6