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Cooperative Differential Evolution with Dynamical Population for Short-Term Traffic Flow Prediction Problem

Volume 14, Number 4, April 2018, pp. 785-794
DOI: 10.23940/ijpe.18.04.p20.785794

Danping Wanga,b,c, Kunyuan Hua, Maowei Hed, and Hanning Chend

aDepartment of Information Service & Intelligent Control, Shenyang Institute of Automation,Chinese Academy of Sciences, Shenyang, 110016, China
bUniversity of Chinese Academy of Sciences, Beijing, 100039, China
cShenyang University, Shenyang, 110044, China
dSchool of Computer Science and Software, Tianjin Polytechnic University, Tianjin, 300387, China

(Submitted on January 13, 2018; Revised on February 16, 2018; Accepted on March 24, 2018)


Differential Evolution (DE) is a heuristic stochastic search algorithm based on population differences, which has advantages of simple parameters and fast convergence rate. However, it has weak robustness, especially for multimodal problems. Therefore, this paper proposes a Cooperative Differential Evolution with Dynamical population (DynCDE). In the proposed algorithm, the K-means method is employed to partition the whole population. For the high convergence rate of DE/current-to-best/ 1/bin, the neighbor-based mutation strategy is applied and the dynamic population size method based on aging mechanism and lifecycle mechanism is designed to keep the balance between exploration and exploitation. This modified DE has the potential to improve prediction accuracy of neural networks. Finally, this DynCDE-based neural network model is applied to solving the short-term traffic flow prediction problem, which offers very excellent results.


References: 9

    1. N. Awad, M. Z. Ali, and R. G. Reynolds, “A differential evolution algorithm with success-based parameter adaptation for CEC2015 learning-based optimization”, in Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 103 - 108, Sendai, Japan, July 2015.
    2. S. Das, A. Mandal, and R. Mukherjee, “An Adaptive Differential Evolution Algorithm for Global Optimization in Dynamic Environments”, IEEE Transactions on Cybernetics, vol. 44, no. 6, pp. 966-978, June 2014.
    3. N. Gupta, “Artificial Neural Network”, Network and Complex Systems, Vol.3, No.1, pp. 1-5, September 2013.
    4. U. Halder, S. Das, D. Maity “A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments”, IEEE Transactions on Cybernetics, vol. 43, no. 3, pp. 881 - 897, June 2013.
    5. L. D. Hang, H. H. Vinh, N. T. Trung, and N. Q. Hung, “A new design approach based on differential evolution algorithm for geometric optimization of magnetorheological brakes”, Smart Materials and Structures, vol. 25, no. 12, pp. 341-344, November 2016.
    6. S. M. Islam, S. Das, S. Ghosh, and S. Roy, “An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 42, no. 2, pp. 482-500, April 2012.
    7. D. Teijeiro, X. C. Pardo, D. R. Penas, P. González, J. R. Banga, and R. Doallo, “A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology”, Cluster Computing, vol.20, no. 3, pp. 1937–1950, September 2017
    8. C. Vaishali, T. K. Sharma, A. Abraham, and J. Rajpurohit, “Trigonometric Probability Tuning in Asynchronous Differential Evolution”, Soft Computing: Theories and Applications (online since November 2017) (DOI 10.1007/978-981-10-5699-4_26).
    9. X. H. Yan, Y. L. Zhu, H. Zhang, H. N. Chen, and B. Niu, “An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning”, Discrete Dynamics in Nature and Society, vol. 10, no 1, pp. 1-20, October 2012.


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