Abstract
Control parameters and mutation methods impact upon the global search ability of differential evolution algorithm (DE), and varying optimization issues own varying parameter settings. In this paper, an enhanced elite archive mutation strategy with self-adaption parameter adjustment (EAMSADE) is proposed to raise DE’s performance. The population’s diversity and the individual’s difference are considered by this paper to enhance the algorithm’s convergence property. EAMSADE amends the DE/rand/1 strategy by means of enhanced elite archive mutation and modifies parameters (crossover rate and scaling factor) adaptively which is based on quantitative analysis of individual variability and population diversity. To confirm the proposed EAMSADE’s performance, a suit of 21 benchmark functions from IEEE CEC2005 are utilized to carry out the experiment. The outcome of the experiment confirms that the proposed EAMSADE has got an overall improvement on convergence performance and global search ability compared to the other four amended DE.
Similar content being viewed by others
References
Gong, Y.J., Chen, W.N., Zhan, Z.H., Zhang, J., Li, Y., Zhang, Q.: Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Appl. Soft Comput. 34, 286–300 (2015)
Wang, L., Yang, B., Orchard, J.: Particle swarm optimization using dynamic tournament topology. Appl. Soft Comput. 48, 584–596 (2016)
Li, H., Demeulemeester, E.: A genetic algorithm for the robust resource leveling problem. J. Sched. 19(1), 43–60 (2016)
Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution—an updated survey. Swarm Evolut. Comput. 27, 1–30 (2016)
Xu, Y., Wang, L., Wang, S.Y., Liu, M.: An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neurocomputing 148, 260–268 (2015)
Mallol-Poyato, R., Jiménez-Fernández, S., Díaz-Villar, P., Salcedo-Sanz, S.: Joint optimization of a microgrid’s structure design and its operation using a two-steps evolutionary algorithm. Energy 94, 775–785 (2016)
Prado, R.S., Silva, R.C.P., Guimarães, F.G., Neto, O.M.: A new differential evolution based metaheuristic for discrete optimization. Int. J. Nat. Comput. Res. 1(2), 15–32 (2017)
Liu, B., Aliakbarian, H., Ma, Z., Vandenbosch, G.A.E., Gielen, G., Excell, P.: An efficient method for antenna design optimization based on evolutionary computation and machine learning techniques. IEEE Trans. Antennas Propag. 62(1), 7–18 (2014)
Tang, L., Zhao, Y., Liu, J.: An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production. IEEE Trans. Evolut. Comput. 18(2), 209–225 (2014)
Liu, B., Aliakbarian, H., Ma, Z., Vandenbosch, G.A.E., Gielen, G., Excell, P.: An efficient method for antenna design optimization based on evolutionary computation and machine learning techniques. IEEE Trans. Antennas Propag. 62(1), 7–18 (2014)
Nama, S., Saha, A.K.: A new hybrid differential evolution algorithm with self-adaptation for function optimization. Appl. Intell. 14, 1–15 (2017)
Elsayed, S.M., Sarker, R.A., Essam, D.L.: Training and testing a self-adaptive multi-operator evolutionary algorithm for constrained optimization. Appl. Soft Comput. J. 26(3), 515–522 (2015)
Elsayed, S., Sarker, R., Coello, C.C., Ray, T.: Adaptation of operators and continuous control parameters in differential evolution for constrained optimization. Soft Comput. 3, 1–22 (2017)
Wu, G., Mallipeddi, R., Suganthan, P.N., Wang, R., Chen, H.: Differential evolution with multi-population based ensemble of mutation strategies. Inf. Sci. 329, 329–345 (2016)
Draa, A., Bouzoubia, S., Boukhalfa, I.: A sinusoidal differential evolution algorithm for numerical optimisation. Appl. Soft Comput. 27(27), 99–126 (2015)
Črepinšek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM. 45(3), 1–33 (2013)
Sun, G., Peng, J., Zhao, R.: Differential evolution with individual-dependent and dynamic parameter adjustment. Soft Comput. 2, 1–27 (2017)
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evolut. Comput. 10(6), 646–657 (2006)
Zou, D., Wu, J., Gao, L., Li, S.: A modified differential evolution algorithm for unconstrained optimization problems. Neurocomputing 120(6), 469–481 (2013)
Zhang, J., Sanderson, A.C.: Jade: adaptive differential evolution with optional external archive. IEEE Trans. Evolut. Comput. 13(5), 945–958 (2009)
Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evolut. Comput. 15(1), 55–66 (2011)
Yi, W., Gao, L., Li, X., Zhou, Y.: A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems. Appl. Intell. 42(4), 642–660 (2015)
Wang, S., Li, Y., Yang, H.: Self-adaptive differential evolution algorithm with improved mutation mode. Soft Comput. 6, 1–15 (2017)
Acknowledgements
The authors acknowledge the National Key Research and Development Project of China (No. 2016YFC1401800) and the Scientific Research Project of NUDT (No. ZK16-03-46, No. ZK16-03-31).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lu, Z., Zhang, L. & Wang, D. Differential evolution with improved elite archive mutation and dynamic parameter adjustment. Cluster Comput 22 (Suppl 4), 9347–9356 (2019). https://doi.org/10.1007/s10586-018-2163-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-2163-6