Abstract
It is well known that mutation scale factor, the crossover constant, and the population size are three main control parameters of the differential evolution (DE) algorithm. These parameters are of great importance to the efficiency of a DE algorithm. However, finding appropriate settings is a difficult task. In this work, a self-adaptive DE with population adjustment scheme (SAPA) is proposed to tune the size of offspring population. The novel algorithm involves two DE strategies and two population adjustment schemes. The performance of the SAPA algorithm is evaluated on a set of benchmark problems. Simulation results show that the proposed algorithm is better than, or at least comparable with, other classic or adaptive DE algorithms. Performance comparisons with some other well-known evolutionary algorithms from literatures are also presented.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Zou D., Liu H., Gao L., Li S.: A novel modified differential evolution algorithm for constrained optimization problems. Comput. Math. Appl. 61(6), 1608–1623 (2011)
Hu C., Yan X.: A hybrid differential evolution algorithm integrated with an ant system and its application. Comput. Math. Appl. 62(1), 32–43 (2011)
Sayah S., Hamouda A., Zehar K.: Economic dispatch using improved differential evolution approach: a case study of the Algerian electrical network. Arabian J. Sci. Eng. 38(3), 715–722 (2013)
Das S., Sil S.: Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm. Inf. Sci. 180(8), 1237–1256 (2010)
Das S., Abraham A., Konar A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. Part A 38(1), 218–237 (2008)
Tasgetiren M.F., Suganthan P.N., Pan Q.-K.: An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem. Appl. Math. Comput. 215(9), 3356–3368 (2010)
Tang Y., Wang Z., Fang J.: Controller design for synchronization of an array of delayed neural networks using a controllable probabilistic PSO. Inf. Sci. 181, 4715–4732 (2011)
Tang Y., Wang Z., Fang J.: Feedback learning particle swarm optimization. Appl. Soft Comput. 11, 4713–4725 (2011)
Abido M.A., Al-Ali N.A.: Multi-objective optimal power flow using differential evolution. Arabian J. Sci. Eng. 37(4), 991–1005 (2012)
Gamperle, R.; Muller, S.D.; Koumoutsakos, P.: A parameter study for differential evolution, in Proceedings of Advanced Intelligent System, Fuzzy Systems, Evolutionary Computation, Crete, Greece, pp. 293–298 (2002)
Zhang, J.; Sanderson, A.C.: An approximate Gaussian model of differential evolution with spherical fitness functions. In: Proceedings of IEEE Congress on Evolutionary Computation, Singapore, pp. 2220–2228 (2007)
Huang, V.L.; Qin, A.K.; Suganthan, P.N.: Self-adaptive differential evolution algorithm for constrained real-parameter optimization. In Proceedings of IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 17–24 (2006)
Brest, J.; Zumer, V.; Maucec, M.S.: Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: Proceedings of IEEE Congress on Evolution Computation, Vancouver, BC, Canada, pp. 215–222 (2006)
Brest J., Boskovic B., Greiner S., Zumer V., Maucec M.S.: Performance comparison of self-adaptive and adaptive differential evolution algorithms, soft computing—a fusion of foundations. Methodol. Appl. 11(7), 617–629 (2007)
Teo J.: Exploring dynamic self-adaptive populations in differential evolution. Soft computing—a fusion of foundations. Methodol. Appl. 10(8), 673–686 (2006)
Yang, Z.; Tang, K.; Yao, X.: Self-adaptive differential evolution with neighborhood search. In: Proceedings of IEEE Congress on Evolution Computation, Hong Kong, China, pp. 1110–1116 (2008)
Qin, A.K.; Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Edinburgh, UK, pp. 1785–1791 (2005)
Qin A.K., Huang V.L., Suganthan P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)
Mallipeddi R., Mallipeddi S., Suganthan P.: Ensemble strategies with adaptive evolutionary programming. Inf. Sci. 180(9), 1571–1581 (2010)
Zhang J.Q., Sanderson A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
Brest J., Greiner S., Boscovic B., Mernik M., Zumer V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)
Rahnamayan S., Tizhoosh H.R., Salama M.M.A.: A novel population initialization method for accelerating evolutionary algorithms. Comput. Math. Appl. 53, 1605–1614 (2007)
Jansen T., Jong K.D., Wegener I.: On the choice of the offspring population size in evolutionary algorithms. Evol. Comput. 13(4), 413–440 (2005)
Tan K.C., Lee T.H., Khor E.F.: Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Trans. Evol. Comput. 5(6), 565–588 (2001)
Eiben, A.E.; Marchiori, E.; Valko, V.A.: Evolutionary algorithms with on-the-fly population size adjustment. In: Proceedings of the 8th International Conference on Parallel Problem Solving from Nature. Lecture Notes in Computer Science, vol. 3242, pp. 41–50 (2004)
Brest J., Maucec M.S.: Population size reduction for the differential evolution algorithm. Appl. Intell. 29(3), 228–247 (2008)
Epitropakis M.G., Tasoulis D.K., Pavlidis N.G.: Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans. Evol. Comput. 15(1), 99–119 (2011)
Nasimul N., Iba H.: Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008)
Suganthan, P.N.; Hansen, N.; Liang, J.J.; Deb, K.; Chen, Y.-P.; Auger, A.; Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Nanyang Technol. Univ., Singapore, IIT Kanpur, Kanpur, India, Tech. Rep. KanGAL (2005)
Satoh, H.; Yamamura, M.; Kobayashi, S.: Minimal generation gap model for GAs considering both exploration and exploitation. In: Proceedings of IIZUKA96, Iizuka, Fukuoka, Japan, pp. 494–497 (1996)
Deb K., Anand A., Joshi D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4), 371–395 (2002)
Ono, I.; Kita, H.; Kobayashi, S.: Advances in Evolutionary Computing. New York: Springer, ch. A Real-Coded Genetic Algorithm Using the Unimodal Normal Distribution Crossover, pp. 213–237 (2003)
Tsutsui, S.; Yamamura, M.; Higuchi, T.: Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Proceedings of Genetic and Evolution Computation, Orlando, Florida, USA, pp. 657–664 (1999)
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper was partially supported by National Natural Science Foundation of China (No. 61271114 and No. 61203325) and Innovation Program of Shanghai Municipal Education Commission (No. 14ZZ068).
Rights and permissions
About this article
Cite this article
Zhao, S., Wang, X., Chen, L. et al. A Novel Self-adaptive Differential Evolution Algorithm with Population Size Adjustment Scheme. Arab J Sci Eng 39, 6149–6174 (2014). https://doi.org/10.1007/s13369-014-1248-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-014-1248-7