Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32301
A Multi-Population Differential Evolution with Adaptive Mutation and Local Search for Global Optimization

Authors: Zhoucheng Bao, Haiyan Zhu, Tingting Pang, Zuling Wang


This paper presents a multi population Differential Evolution (DE) with adaptive mutation and local search for global optimization, named AMMADE in order to better coordinate the cooperation between the populations and the rational use of resources. In AMMADE, the population is divided based on the Euclidean distance sorting method at each generation to appropriately coordinate the cooperation between subpopulations and the usage of resources, such that the best-performed subpopulation will get more computing resources in the next generation. Further, an adaptive local search strategy is employed on the best-performed subpopulation to achieve a balanced search. The proposed algorithm has been tested by solving optimization problems taken from CEC2014 benchmark problems. Experimental results show that our algorithm can achieve a competitive or better result than related methods. The results also confirm the significance of devised strategies in the proposed algorithm.

Keywords: Differential evolution, multi-mutation strategies, memetic algorithm, adaptive local search.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 236


[1] K. M. Sallam, S. M. Elsayed, R. K. Chakrabortty, and M. Ryan, “Evolutionary framework with reinforcement learning-based mutation adaptation,” IEEE Access, vol. 8, 2020.
[2] X. F. Liu, Z. H. Zhan, Y. Lin, W. N. Chen, Y. J. Gong, T. L. Gu, H. Q. Yuan, and J. Zhang, “Historical and heuristic-based adaptive differential evolution,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. PP, pp. 1–13, 2018.
[3] S. Kitayama, M. Arakawa, and K. Yamazaki, “Differential evolution as the global optimization technique and its application to structural optimization,” Applied Soft Computing, vol. 11, no. 4, pp. 3792–3803, 2011.
[4] R. Mallipeddi and P. N. Suganthan, “Differential evolution algorithm with ensemble of parameters and mutation and crossover strategies,” in Swarm, Evolutionary, and Memetic Computing - First International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2010, Chennai, India, December 16-18, 2010. Proceedings, 2010.
[5] J. Zhang and A. C. Sanderson, “Jade: adaptive differential evolution with optional external archive,” IEEE Transactions on evolutionary computation, vol. 13, no. 5, pp. 945–958, 2009.
[6] W. Yong, Z. Cai, and Q. Zhang, “Differential evolution with composite trial vector generation strategies and control parameters,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 55–66, 2011.
[7] A. K. Qin and P. N. Suganthan, “Self-adaptive differential evolution algorithm for numerical optimization,” in IEEE Congress on Evolutionary Computation, 2005.
[8] X. Li, L. Wang, Q. Jiang, and N. Li, “Differential evolution algorithm with multi-population cooperation and multi-strategy integration,” Neurocomputing, vol. 421, no. 1, pp. 285–302, 2021.
[9] G. Wu, R. Mallipeddi, P. N. Suganthan, W. Rui, and H. Chen, “Differential evolution with multi-population based ensemble of mutation strategies,” Information Sciences An International Journal, vol. 329, no. C, pp. 329–345, 2016.
[10] M. M. Mafarja and S. Mirjalili, “Hybrid whale optimization algorithm with simulated annealing for feature selection,” Neurocomputing, vol. 260, pp. 302–312, 2017.
[Online]. Available:
[11] T. Rogalsky and R. W. Derksen, “Hybridization of differential evolution for aerodynamic design,” 2000.
[12] D. Molina, A. Latorre, and F. Herrera, “Shade with iterative local search for large-scale global optimization,” in 2018 IEEE Congress on Evolutionary Computation (CEC), 2018.
[13] R. Tanabe and A. Fukunaga, “Evaluating the performance of shade on cec 2013 benchmark problems,” in 2013 IEEE Congress on evolutionary computation. IEEE, 2013, pp. 1952–1959.
[14] J. Brito, L. Ochi, F. Montenegro, and N. Maculan, “An iterative local search approach applied to the optimal stratification problem,” International Transactions in Operational Research, vol. 17, no. 6, pp. 753–764, 2010.
[15] X. Wang, M. Sheng, K. Ye, J. Lin, J. Mao, S. Chen, and W. Sheng, “A multilevel sampling strategy based memetic differential evolution for multimodal optimization,” Neurocomputing, vol. 334, no. MAR.21, pp. 79–88, 2019.