A Multi-Population Differential Evolution with Adaptive Mutation and Local Search for Global Optimization
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.
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“Evolutionary framework with reinforcement learning-based mutation
adaptation,” IEEE Access, vol. 8, 2020.
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Yuan, and J. Zhang, “Historical and heuristic-based adaptive differential
evolution,” IEEE Transactions on Systems, Man, and Cybernetics:
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as the global optimization technique and its application to structural
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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:
https://www.sciencedirect.com/science/article/pii/S092523121730807X
[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.
[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:
https://www.sciencedirect.com/science/article/pii/S092523121730807X
[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.
@article{"International Journal of Information, Control and Computer Sciences:80875", author = "Zhoucheng Bao and Haiyan Zhu and Tingting Pang and Zuling Wang", title = "A Multi-Population Differential Evolution with Adaptive Mutation and Local Search for Global Optimization", abstract = "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.", volume = "16", number = "8", pages = "273-7", }
