Differential evolution (DE) is one of the most successful evolutionary algorithms. However, the performance of DE is significantly influenced by its mutation strategies. Generally, different mutation strategies may obtain different search directions. The improper search direction misleads the search and results in the poor performance of DE. Therefore, it is vital to consider the search direction when designing new mutation strategies. Based on this consideration, in this paper, the quasi-reflection-based mutation is proposed to enhance the performance of DE. The quasi-reflection-based mutation is able to provide the promising search direction to guide the search. To extensively evaluate the performance of our approach, $ 30 $ benchmark functions are chosen as the test suite. Combined with SHADE, Re-SHADE is presented. Compared with different advanced DE methods, Re-SHADE can obtain better results in terms of the accuracy and the convergence rate. Additionally, further experiments on the CEC2013 test suite also confirm the effectiveness of the proposed method.
Citation: Wei Li, Wenyin Gong. Differential evolution with quasi-reflection-based mutation[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2425-2441. doi: 10.3934/mbe.2021123
Differential evolution (DE) is one of the most successful evolutionary algorithms. However, the performance of DE is significantly influenced by its mutation strategies. Generally, different mutation strategies may obtain different search directions. The improper search direction misleads the search and results in the poor performance of DE. Therefore, it is vital to consider the search direction when designing new mutation strategies. Based on this consideration, in this paper, the quasi-reflection-based mutation is proposed to enhance the performance of DE. The quasi-reflection-based mutation is able to provide the promising search direction to guide the search. To extensively evaluate the performance of our approach, $ 30 $ benchmark functions are chosen as the test suite. Combined with SHADE, Re-SHADE is presented. Compared with different advanced DE methods, Re-SHADE can obtain better results in terms of the accuracy and the convergence rate. Additionally, further experiments on the CEC2013 test suite also confirm the effectiveness of the proposed method.
[1] | R. Storn, K. Price, Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11 (1997), 341-359. doi: 10.1023/A:1008202821328 |
[2] | S. Das, P. Suganthan, Differential evolution: A survey of the state-of-the-art, IEEE Trans. Evol. Comput., 15 (2011), 4-31. doi: 10.1109/TEVC.2010.2059031 |
[3] | S. Das, S. Mullick, P. Suganthan, Recent advances in differential evolution-An updated survey, Swarm Evol. Comput., 27 (2016), 1-30. doi: 10.1016/j.swevo.2016.01.004 |
[4] | J. Jang, K. Jang, H. Kwon, J. Lee, Feedback control of an HBV model based on ensemble kalman filter and differential evolution, Math. Biosci. Eng., 15 (2018), 667-691. doi: 10.3934/mbe.2018030 |
[5] | M. Bilal, H. Zaheer, L. Garcia-Hernandez, A. Abraham, Differential evolution: A review of more than two decades of research, Eng. Appl. Artif. Intell., 90 (2020), 103479. doi: 10.1016/j.engappai.2020.103479 |
[6] | K. Price, R. Storn, J. Lampinen, Differential Evolution: A Practical Approach to Global Optimization, Springer-Verlag, Berlin, 2005. |
[7] | Y. Li, S. Wang, B. Yang, An improved differential evolution algorithm with dual mutation strategies collaboration, Expert Syst. Appl., 153 (2020), 113451. doi: 10.1016/j.eswa.2020.113451 |
[8] | J. Nelder, R. Mead, A simplex method for function minimization, Comput. J., 7 (1965), 308-313. doi: 10.1093/comjnl/7.4.308 |
[9] | R. Tanabe, A. Fukunaga, Success-history based parameter adaptation for differential evolution, in 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, 71-78. |
[10] | J. Zhang, A. Sanderson, JADE: Adaptive differential evolution with optional external archive, IEEE Trans. Evol. Comput., 13 (2009), 945-958, . doi: 10.1109/TEVC.2009.2014613 |
[11] | J. Zhang, A. Sanderson, Adaptive Differential Evolution: A Robust Approach to Multimodal Problem Optimization, Springer-Verlag, Berlin, 2009. |
[12] | A. Mohamed, A. Mohamed, Adaptive guided differential evolution algorithm with novel mutation for numerical optimization, Int. J. Mach. Learn. & Cyber., 10 (2019), 253-277. |
[13] | A. Mohamed, A. Hadi, K. Jambi, Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization, Swarm Evol. Comput., 50 (2019), 100455. doi: 10.1016/j.swevo.2018.10.006 |
[14] | R. Tanabe, A. Fukunaga, Improving the search performance of SHADE using linear population size reduction, in 2014 IEEE Congress on Evolutionary Computation (CEC), 2014, 1658-1665. |
[15] | A. Ghosh, S. Das, A. K. Das, L. Gao, Reusing the past difference vectors in differential evolution - A simple but significant improvement, IEEE Trans. on Cybern., 50 (2020), 4821-4834. doi: 10.1109/TCYB.2019.2921602 |
[16] | M. Ali, M. Pant, A. Abraham, Simplex differential evolution, Acta Polytech. Hungarica., 6 (2009), 95-115. |
[17] | Z. Gao, T. Xiao, W. Fan, Hybrid differential evolution and Nelder-Mead algorithm with re-optimization, Soft Comput., 15 (2011), 581-594. doi: 10.1007/s00500-010-0566-2 |
[18] | J. Brest, S. Greiner, B. Bošković, M. Mernik, V. Žumer, Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE Trans. Evol. Comput., 10 (2006), 646-657. doi: 10.1109/TEVC.2006.872133 |
[19] | X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, IEEE Trans. Evol. Comput., 3 (1999), 82-102. doi: 10.1109/4235.771163 |
[20] | S. Rahnamayan, H. Tizhoosh, M. Salama, Opposition-based differential evolution, IEEE Trans. Evol. Comput., 12 (2008), 64-79. doi: 10.1109/TEVC.2007.894200 |
[21] | M. Ali, C. Khompatraporn, Z. Zabinsky, A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems, J. Global Optim., 31 (2005), 635-672. doi: 10.1007/s10898-004-9972-2 |
[22] | W. Gong, A. Zhou, Z. Cai, A multioperator search strategy based on cheap surrogate models for evolutionary optimization, IEEE Trans. Evol. Comput., 19 (2015), 746-758. doi: 10.1109/TEVC.2015.2449293 |
[23] | Y. Wang, H. X. Li, T. Huang, L. Li, Differential evolution based on covariance matrix learning and bimodal distribution parameter setting, Appl. Soft Comput., 18 (2014), 232-247. doi: 10.1016/j.asoc.2014.01.038 |
[24] | J. Alcalá-Fdez, L. Sánchez, S. García, M. del Jesus, S. Ventura, J. M. Garrell, et al., KEEL: A software tool to assess evolutionary algorithms for data mining problems, Soft Comput., 13 (2009), 307-318. doi: 10.1007/s00500-008-0323-y |
[25] | J. Liang, B, Qu, P. Suganthan, A. Hernández-Díaz, Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization, Technical Report 201212, Computational Intelligence Laboratory, Zhengzhou University, 2013. |
[26] | N. Qiu, Q. Liu, Q. Gao, Q. Zeng, Combining genetic algorithm and generalized least squares for geophysical potential field data optimized inversion, IEEE Geosci. Remote. Sens. Lett., 7 (2010), 660-664. doi: 10.1109/LGRS.2010.2045152 |
[27] | C. Hu, J. Cai, D. Zeng, X. Yan, W. Gong, L. Wang, Deep reinforcement learning based valve scheduling for pollution isolation in water distribution network, Math. Biosci. Eng., 17 (2020), 105-121. doi: 10.3934/mbe.2020006 |
[28] | A. Alejo-Reyes, E. Olivares-Benitez, A. Mendoza, A. Rodriguez, Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms, Math. Biosci. Eng., 17 (2020), 2016-2036. doi: 10.3934/mbe.2020107 |
[29] | X. Peng, H. Jia, C. Lang, Modified dragonfly algorithm based multilevel thresholding method for color images segmentation, Math. Biosci. Eng., 16 (2019), 6467-6511. doi: 10.3934/mbe.2019324 |
mbe-18-03-123-Supplementary.pdf |