Differential evolution
algorithm based on the covariance matrix learning can adjust the coordinate
system according to the characteristics of the population, which makes the search move in a more
favorable direction. In order to obtain more accurate information about the
function shape, this paper proposescovariance matrix learning
differential evolution algorithm based on correlation (denoted as RCLDE)to improve the search
efficiency of the algorithm. First, a hybrid mutation strategy is designed to
balance the diversity and convergence of the population; secondly, the
covariance learning matrix is constructed by selecting the individual with the
less correlation; then, a comprehensive learning mechanism is comprehensively
designed by two covariance matrix learning mechanisms based on the principle of
probability. Finally,the
algorithm is tested on the CEC2005, and the experimental results are compared
with other effective differential evolution algorithms. The experimental
results show that the algorithm proposed in this paper is an effective algorithm.
References
[1]
Storn, R. and Price, K. (1997) Differential Evolution: A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11, 341-359. https://doi.org/10.1023/A:1008202821328
[2]
Ding, Q.F. and Xin, X.Y. (2017) Research Survey of Differential Evolution Algorithms. CAAI Transactions on Intelligent Systems, 4, 431-442.
[3]
Zhang, J. (2009) Jade: Adaptive Differential Evolution with Optional External Archive. IEEE Transactions on Evolutionary Computation, 13, 945-958.
https://doi.org/10.1109/TEVC.2009.2014613
[4]
Wang, Y., Cai, Z. and Zhang, Q. (2011) Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters. IEEE Transactions on Evolutionary Computation, 15, 55-66. https://doi.org/10.1109/TEVC.2010.2087271
[5]
Wang, Y., Li, H.X., Huang, T. and Li, L. (2014) Differential Evolution Based on Covariance Matrix Learning and Bimodal Distribution Parameter Setting. Applied Soft Computing Journal, 18, 232-247. https://doi.org/10.1016/j.asoc.2014.01.038
[6]
Li, Y.L., Feng, J.F. and Hu, J.H. (2016) Covariance and Crossover Matrix Guided Differential Evolution for Global Numerical Optimization. Springerplus, 5, 1176.
https://doi.org/10.1186/s40064-016-2838-5
[7]
Awad, N.H., Ali, M.Z. and Suganthan, P.N. (2017) Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood for Solving CEC2017 Benchmark Problems. 2017 IEEE Congress on Evolutionary Computation (CEC), San Sebastian, 5-8 June 2017, 372-379. https://doi.org/10.1109/CEC.2017.7969336
[8]
Li, Y., Wang, S. and Yang, B. (2020) An Improved Differential Evolution Algorithm with Dual Mutation Strategies Collaboration. Expert Systems with Applications, 153, 113-451. https://doi.org/10.1016/j.eswa.2020.113451
[9]
Suganthan, P.N., Hansen, N., Liang, J.J., et al. (2005) Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Natural Computing, 341-357.
[10]
Brest, J., Greiner, S., Boskovic, B., Mernik, M. and Zumer, V. (2006) Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation, 10, 646-657.
https://doi.org/10.1109/TEVC.2006.872133
[11]
Brest, J., Zumer, V. and Maucec, M.S. (2006) Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization. IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, BC, 16-21 July 2006, 215-222.
https://doi.org/10.1109/CEC.2006.1688311
[12]
Mann, H.B. and Whitney, D.R. (19476) On a Test of Whether One of Two Random Variables Is Stochastically Larger Than the Other. Annals of Mathematical Statistics, 18, 50-60. https://doi.org/10.1214/aoms/1177730491
[13]
Schach, S., et al. (1979) An Alternative to the Friedman Test with Certain Optimality Properties. The Annals of Statistics, 7, 537-550.
https://doi.org/10.1214/aos/1176344675
