A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
S. Gupta, S. Singh, R. Su, S. Gao, and J. C. Bansal, “Multiple elite individual guided piecewise search-based differential evolution,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 135–158, Jan. 2023. doi: 10.1109/JAS.2023.123018
Citation: S. Gupta, S. Singh, R. Su, S. Gao, and J. C. Bansal, “Multiple elite individual guided piecewise search-based differential evolution,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 135–158, Jan. 2023. doi: 10.1109/JAS.2023.123018

Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution

doi: 10.1109/JAS.2023.123018
Funds:  This work was supported by the A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre-Positioning (IAF-PP) (Award A19D6a0053) and the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP22H03643)
More Information
  • The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters’ selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.

     

  • loading
  • [1]
    X. J. Yu and M. Gen, Introduction to Evolutionary Algorithms. London, UK: Springer, 2010.
    [2]
    T. Back, U. Hammel, and H. Schwefel, “Evolutionary computation: Comments on the history and current state,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 3–17, Apr. 1997. doi: 10.1109/4235.585888
    [3]
    J. Bi, H. T. Yuan, J. H. Zhai, M. C. Zhou, and H. V. Poor, “Self-adaptive bat algorithm with genetic operations,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1284–1294, Jul. 2022. doi: 10.1109/JAS.2022.105695
    [4]
    W. J. Gu, Y. G. Yu, and W. Hu, “Artificial bee colony algorithmbased parameter estimation of fractional-order chaotic system with time delay,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 107–113, Jan. 2017. doi: 10.1109/JAS.2017.7510340
    [5]
    J. Zhao, S. X. Liu, M. C. Zhou, X. W. Guo, and L. Qi, “Modified cuckoo search algorithm to solve economic power dispatch optimization problems,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 4, pp. 794–806, Jul. 2018. doi: 10.1109/JAS.2018.7511138
    [6]
    J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. ICNN’95-Int. Conf. Neural Networks, Perth, Australia, pp. 1942–1948.
    [7]
    R. Storn and K. Price, “Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces,” J. Glob. Optim., vol. 11, no. 4, pp. 341–359, Jan. 1997. doi: 10.1023/A:1008202821328
    [8]
    M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, Nov. 2006. doi: 10.1109/MCI.2006.329691
    [9]
    D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” J. Glob. Optim., vol. 39, no. 3, pp. 459–471, Nov. 2007. doi: 10.1007/s10898-007-9149-x
    [10]
    S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, Mar. 2014. doi: 10.1016/j.advengsoft.2013.12.007
    [11]
    Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A new heuristic optimization algorithm: Harmony search,” Simulation, vol. 76, no. 2, pp. 60–68, Feb. 2001. doi: 10.1177/003754970107600201
    [12]
    M. Mahalakshmi, P. Kalaivani, and E. K. Nesamalar, “A review on genetic algorithm and its applications,” Int. J. Comput. Algorithm, vol. 2, no. 2, pp. 415–423, 2013.
    [13]
    S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, “Differential evolution using a neighborhood-based mutation operator,” IEEE Trans. Evol. Comput., vol. 13, no. 3, pp. 526–553, Jun. 2009. doi: 10.1109/TEVC.2008.2009457
    [14]
    E. V. Altay and B. Alatas, “Differential evolution and sine cosine algorithm based novel hybrid multi-objective approaches for numerical association rule mining,” Inf. Sci., vol. 554, pp. 198–221, Apr. 2021. doi: 10.1016/j.ins.2020.12.055
    [15]
    J. J. Liang, B. Y. Qu, and P. N. Suganthan, “Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization,” Zhengzhou University, Zhengzhou, China, Technical Report 201311, 2013.
    [16]
    N. H. Awad, M. Z. Ali, J. J. Liang, B. Y. Qu, and P. N. Suganthan, “Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization,” Nanyang Technological University, Singapore and Jordan University of Science and Technology, Jordan and Zhengzhou University, Zhengzhou, China, 2016.
    [17]
    Y. Yu, S. Gao, Y. R. Wang, and Y. Todo, “Global optimum-based search differential evolution,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 379–394, Mar. 2019. doi: 10.1109/JAS.2019.1911378
    [18]
    Y. Yu, Z. Y. Lei, Y. R. Wang, T. F. Zhang, C. Peng, and S. Gao, “Improving dendritic neuron model with dynamic scale-free network-based differential evolution,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 99–110, Jan. 2022. doi: 10.1109/JAS.2021.1004284
    [19]
    H. Y. Fan and J. Lampinen, “A trigonometric mutation operation to differential evolution,” J. Glob. Optim., vol. 27, no. 1, pp. 105–129, Sep. 2003. doi: 10.1023/A:1024653025686
    [20]
    J. Q. Zhang and A. C. Sanderson, “JADE: Adaptive differential evolution with optional external archive,” IEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 945–958, Oct. 2009. doi: 10.1109/TEVC.2009.2014613
    [21]
    W. Y. Gong and Z. H. Cai, “Differential evolution with ranking-based mutation operators,” IEEE Trans. Cybern., vol. 43, no. 6, pp. 2066–2081, Dec. 2013. doi: 10.1109/TCYB.2013.2239988
    [22]
    J. H. Wang, J. J. Liao, Y. Zhou, and Y. Q. Cai, “Differential evolution enhanced with multiobjective sorting-based mutation operators,” IEEE Trans. Cybern., vol. 44, no. 12, pp. 2792–2805, Dec. 2014. doi: 10.1109/TCYB.2014.2316552
    [23]
    J. C. Cheng, Z. B. Pan, H. Liang, Z. Q. Gao, and J. H. Gao, “Differential evolution algorithm with fitness and diversity ranking-based mutation operator,” Swarm Evol. Comput., vol. 61, p. 100816, Mar. 2021. doi: 10.1016/j.swevo.2020.100816
    [24]
    X. Zhang and S. Y. Yuen, “A directional mutation operator for differential evolution algorithms,” Appl. Soft Comput., vol. 30, pp. 529–548, May 2015. doi: 10.1016/j.asoc.2015.02.005
    [25]
    G. J. Sun, Y. F. Lan, and R. Q. Zhao, “Differential evolution with Gaussian mutation and dynamic parameter adjustment,” Soft Comput., vol. 23, no. 5, pp. 1615–1642, Mar. 2019. doi: 10.1007/s00500-017-2885-z
    [26]
    M. Pant, R. Thangaraj, A. Abraham, and C. Grosan, “Differential evolution with Laplace mutation operator,” in Proc. IEEE Congr. Evolutionary Computation, Trondheim, Norway, 2009, pp. 2841–2849.
    [27]
    J. C. Y. Lai, F. H. F. Leung, and S. H. Ling, “A new differential evolution with wavelet theory based mutation operation,” in Proc. IEEE Congr. Evolutionary Computation, Trondheim, Norway, 2009, pp. 1116–1122.
    [28]
    G. Sun and Y. Q. Cai, “A novel neighborhood-dependent mutation operator for differential evolution,” in Proc. IEEE Int. Conf. Computational Science and Engineering and IEEE Int. Conf. Embedded and Ubiquitous Computing, Guangzhou, China, 2017, pp. 837–841.
    [29]
    S. Biswas, S. Kundu, and S. Das, “An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution,” IEEE Trans. Cybern., vol. 44, no. 10, pp. 1726–1737, Oct. 2014. doi: 10.1109/TCYB.2013.2292971
    [30]
    Y. Z. Zhou, X. Y. Li, and L. Gao, “A differential evolution algorithm with intersect mutation operator,” Appl. Soft Comput., vol. 13, no. 1, pp. 390–401, Jan. 2013. doi: 10.1016/j.asoc.2012.08.014
    [31]
    M. Y. Ameca-Alducin, E. Mezura-Montes, and N. Cruz-Ramírez, “A repair method for differential evolution with combined variants to solve dynamic constrained optimization problems,” in Proc. Annu. Conf. Genetic and Evolutionary Computation, Madrid, Spain, 2015, pp. 241–248.
    [32]
    R. Mallipeddi, N. Suganthan, Q. K. Pan, and M. F. Tasgetiren, “Differential evolution algorithm with ensemble of parameters and mutation strategies,” Appl. Soft Comput., vol. 11, no. 2, pp. 1679–1696, Mar. 2011. doi: 10.1016/j.asoc.2010.04.024
    [33]
    Y. Wang, Z. X. Cai, and Q. F. Zhang, “Differential evolution with composite trial vector generation strategies and control parameters,” IEEE Trans. Evol. Comput., vol. 15, no. 1, pp. 55–66, Feb. 2011. doi: 10.1109/TEVC.2010.2087271
    [34]
    M. Pant, M. Ali, and V. P. Singh, “Differential evolution with parent centric crossover,” in Proc. 2nd UKSIM European Symp. Computer Modeling and Simulation, Liverpool, UK, 2008, pp. 141–146.
    [35]
    Y. Wang, Z. X. Cai, and Q. F. Zhang, “Enhancing the search ability of differential evolution through orthogonal crossover,” Inf. Sci., vol. 185, no. 1, pp. 153–177, Feb. 2012. doi: 10.1016/j.ins.2011.09.001
    [36]
    Y. Q. Cai and J. H. Wang, “Differential evolution with hybrid linkage crossover,” Inf. Sci., vol. 320, pp. 244–287, Nov. 2015. doi: 10.1016/j.ins.2015.05.026
    [37]
    R. Storn and K. Price, “Minimizing the real functions of the ICEC’96 contest by differential evolution,” in Proc. IEEE Int. Conf. Evolutionary Computation, Nagoya, Japan, 1996, pp. 842–844.
    [38]
    R. Gamperle, S. D. Muller, and P. Koumoutsakos, “A parameter study for differential evolution,” in Proc. WSEAS Int. Conf. Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, Interlaken, Switzerland, 2002, pp. 293–298.
    [39]
    J. Ronkkonen, S. Kukkonen, and K. V. Price, “Real-parameter optimization with differential evolution,” in Proc. IEEE Congr. Evolutionary Computation, Edinburgh, UK, 2005, pp. 506–513.
    [40]
    Z. W. Zhao, J. M. Yang, Z. Y. Hu, and H. J. Che, “A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems,” Eur. J. Oper. Res., vol. 250, no. 1, pp. 30–45, Apr. 2016. doi: 10.1016/j.ejor.2015.10.043
    [41]
    A. K. Qin, V. L. Huang, and N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Trans. Evol. Comput., vol. 13, no. 2, pp. 398–417, Apr. 2009. doi: 10.1109/TEVC.2008.927706
    [42]
    A. Ghosh, S. Das, A. Chowdhury, and R. Giri, “An improved differential evolution algorithm with fitness-based adaptation of the control parameters,” Inf. Sci., vol. 181, no. 18, pp. 3749–3765, Sep. 2011. doi: 10.1016/j.ins.2011.03.010
    [43]
    A. Ortiz, J. A. Cabrera, F. Nadal, and A. Bonilla, “Dimensional synthesis of mechanisms using differential evolution with auto-adaptive control parameters,” Mech. Mach. Theory, vol. 64, pp. 210–229, Jun. 2013. doi: 10.1016/j.mechmachtheory.2013.02.002
    [44]
    J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer, “Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems,” IEEE Trans. Evol. Comput., vol. 10, no. 6, pp. 646–657, Dec. 2006. doi: 10.1109/TEVC.2006.872133
    [45]
    H. X. Guo, Y. N. Li, J. L. Li, H. Sun, D. Y. Wang, and X. H. Chen, “Differential evolution improved with self-adaptive control parameters based on simulated annealing,” Swarm Evol. Comput., vol. 19, pp. 52–67, Dec. 2014. doi: 10.1016/j.swevo.2014.07.001
    [46]
    A. Zamuda and J. Brest, “Self-adaptive control parameters’ randomization frequency and propagations in differential evolution,” Swarm Evol. Comput., vol. 25, pp. 72–99, Dec. 2015. doi: 10.1016/j.swevo.2015.10.007
    [47]
    G. J. Sun, G. N. Xu, and N. Jiang, “A simple differential evolution with time-varying strategy for continuous optimization,” Soft Comput., vol. 24, no. 4, pp. 2727–2747, Feb. 2020. doi: 10.1007/s00500-019-04159-0
    [48]
    J. Q. Zhang, X. X. Zhu, Y. H. Wang, and M. C. Zhou, “Dual-environmental particle swarm optimizer in noisy and noise-free environments,” IEEE Trans. Cybern., vol. 49, no. 6, pp. 2011–2021, Jun. 2019. doi: 10.1109/TCYB.2018.2817020
    [49]
    S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Opposition-based differential evolution,” IEEE Trans. Evol. Comput., vol. 12, no. 1, pp. 64–79, Feb. 2008. doi: 10.1109/TEVC.2007.894200
    [50]
    A. Draa, S. Bouzoubia, and I. Boukhalfa, “A sinusoidal differential evolution algorithm for numerical optimisation,” Appl. Soft Comput., vol. 27, pp. 99–126, Feb. 2015. doi: 10.1016/j.asoc.2014.11.003
    [51]
    W. Deng, S. F. Shang, X. Cai, H. M. Zhao, Y. J. Song, and J. J. Xu, “An improved differential evolution algorithm and its application in optimization problem,” Soft Comput., vol. 25, no. 7, pp. 5277–5298, Apr. 2021. doi: 10.1007/s00500-020-05527-x
    [52]
    G. H. Wu, R. Mallipeddi, N. Suganthan, R. Wang, and H. K. Chen, “Differential evolution with multi-population based ensemble of mutation strategies,” Inf. Sci., vol. 329, pp. 329–345, Feb. 2016. doi: 10.1016/j.ins.2015.09.009
    [53]
    C. J. Xu, H. Huang, and S. J. Ye, “A differential evolution with replacement strategy for real-parameter numerical optimization,” in Proc. IEEE Congr. Evolutionary Computation, Beijing, China, 2014, pp. 1617–1624.
    [54]
    Y. R. Wang, S. Gao, M. C. Zhou, and Y. Yu, “A multi-layered gravitational search algorithm for function optimization and real-world problems,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 94–109, Jan. 2021. doi: 10.1109/JAS.2020.1003462
    [55]
    J. X. Lu, X. Y. Zhou, Y. Ma, M. W. Wang, J. Y. Wan, and W. J. Wang, “A novel artificial bee colony algorithm with division of labor for solving CEC 2019 100-digit challenge benchmark problems,” in Proc. IEEE Congr. Evolutionary Computation, Wellington, New Zealand, 2019, pp. 387–394.
    [56]
    F. Wilcoxon, “Individual comparisons by ranking methods,” in Breakthroughs in Statistics, S. Kotz and N. L. Johnson, Eds. New York, USA: Springer, 1992.
    [57]
    J. Luengo, S. García, and F. Herrera, “A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests,” Expert Syst. Appl., vol. 36, no. 4, pp. 7798–7808, May 2009. doi: 10.1016/j.eswa.2008.11.041
    [58]
    O. Olorunda and A. P. Engelbrecht, “Measuring exploration/exploitation in particle swarms using swarm diversity,” in Proc. IEEE Congr. Evolutionary Computation (IEEE World Congr. Computational Intelligence), Hong Kong, China, 2008, pp. 1128–1134.
    [59]
    B. Morales-Castañeda, D. Zaldívar, E. Cuevas, F. Fausto, and A. Rodríguez, “A better balance in metaheuristic algorithms: Does it exist,” Swarm Evol. Comput., vol. 54, p. 100671, May 2020. doi: 10.1016/j.swevo.2020.100671
    [60]
    A. H. Gandomi, X. S. Yang, and A. H. Alavi, “Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems,” Eng. Comput., vol. 29, no. 1, pp. 17–35, Apr. 2013. doi: 10.1007/s00366-011-0241-y
    [61]
    M. J. Cui, L. Li, M. C. Zhou, and A. Abusorrah, “Surrogate-assisted autoencoder-embedded evolutionary optimization algorithm to solve high-dimensional expensive problems,” IEEE Trans. Evol. Comput., vol. 26, no. 4, pp. 676–689, Aug. 2022. doi: 10.1109/TEVC.2021.3113923
    [62]
    H. Pan, L. Wang, and B. Liu, “Particle swarm optimization for function optimization in noisy environment,” Appl. Math. Comput., vol. 181, no. 2, pp. 908–919, Oct. 2006.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(8)

    Article Metrics

    Article views (358) PDF downloads(51) Cited by()

    Highlights

    • A new Differential Evolution (DE) variant called MIDE is proposed
    • The MIDE proposed a new mutation operator to perform a piecewise search in DE
    • Control parameters are tuned based on their success rates in past evolutionary stages
    • Extensive experiments and comparisons validate the efficiency of the MIDE

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return