1,850
Views
3
CrossRef citations to date
0
Altmetric
Advanced Machine Learning and Optimization Theories and Algorithms for Heterogeneous Data Analytics

An adaptive differential evolution algorithm using fitness distance correlation and neighbourhood-based mutation strategy

, , ORCID Icon &
Pages 829-856 | Received 28 Feb 2021, Accepted 18 Oct 2021, Published online: 29 Nov 2021

References

  • Aurenhammer, F. (1991). Voronoi diagrams – A survey of a fundamental geometric data structure. ACM Computing Surveys (CSUR), 23(3), 345–405. https://doi.org/10.1145/116873.116880
  • Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10(6), 646–657. https://doi.org/10.1109/TEVC.2006.872133
  • Choudhary, N., Sharma, H., & Sharma, N. (2016). Differential evolution algorithm using stochastic mutation. In Paper presented at the 2016 international conference on computing, communication and automation (ICCCA). IEEE.
  • Corder, G. W., & Foreman, D. I. (2009). Nonparametric statistics for non-statisticians (a step-by-step approach) | Nonparametric statistics: an Introduction. pp. 1–11. John Wiley & Sons, Inc.
  • Cui, L., Li, G., Zhu, Z., Lin, Q., Wong, K. C., Chen, J., & Lu, J. (2018). Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism. Information Sciences, 422, 122–143. https://doi.org/10.1016/j.ins.2017.09.002
  • Das, S., Abraham, A., Chakraborty, U. K., & Konar, A. (2009). Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computation, 13(3), 526–553. https://doi.org/10.1109/TEVC.2008.2009457
  • Deng, W., Shang, S., Cai, X., Zhao, H., Song, Y., & Xu, J. (2021). An improved differential evolution algorithm and its application in optimization problem. Soft Computing, 25(7), 5277–5298. https://doi.org/10.1007/s00500-020-05527-x
  • Dos Santos Coelho, L., Mariani, V. C., Da Luz, M. V. F., & Leite, J. V. (2013). Novel gamma differential evolution approach for multiobjective transformer design optimization. IEEE Transactions on Magnetics, 49(5), 2121–2124. https://doi.org/10.1109/TMAG.2013.2243134
  • ElQuliti, S. A. H., & Mohamed, A. W. (2016). A large-scale nonlinear mixed-binary goal programming model to assess candidate locations for solar energy stations: an improved real-binary differential evolution algorithm with a case study. Journal of Computational and Theoretical Nanoscience, 13(11), 7909–7921. https://doi.org/10.1166/jctn.2016.5791
  • Guo, H., Li, Y., Li, J., Sun, H., Wang, D., & Chen, X. (2014). Differential evolution improved with self-adaptive control parameters based on simulated annealing. Swarm and Evolutionary Computation, 19, 52–67. https://doi.org/10.1016/j.swevo.2014.07.001
  • Hadi, A. A., Mohamed, A. W., & Jambi, K. M. (2019). LSHADE-SPA memetic framework for solving large-scale optimization problems. Complex and Intelligent Systems, 5(1), 25–40. https://doi.org/10.1007/s40747-018-0086-8
  • Hadi, A. A., Mohamed, A. W., & Jambi, K. M. (2021). Single-objective real-parameter optimization: enhanced LSHADE-SPACMA algorithm. In Heuristics for optimization and learning (pp. 103–121). Springer.
  • Hancer, E. (2019). Fuzzy kernel feature selection with multi-objective differential evolution algorithm. Connection Science, 31(4), 323–341. https://doi.org/10.1080/09540091.2019.1639624
  • Hou, Y., Zhao, L., & Lu, H. (2018). Fuzzy neural network optimization and network traffic forecasting based on improved differential evolution. Future Generation Computer Systems, 81, 425–432. https://doi.org/10.1016/j.future.2017.08.041
  • Huang, Y., Li, W., Tian, F., & Meng, X. (2020). A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy. Applied Soft Computing, 96, 106693. https://doi.org/10.1016/j.asoc.2020.106693
  • Jiang, J., Xue, Y., Ma, T., & Chen, Z. (2018). Improved artificial bee colony algorithm with differential evolution for the numerical optimisation problems. International Journal of Computational Science and Engineering, 16(1), 73–84. https://doi.org/10.1504/IJCSE.2018.089584
  • Jones, T. C., & Forrest, S. (1995). Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Paper presented at the Proceedings of the sixth international conference on genetic algorithms. Santa Fe Institute.
  • Kennedy, J., & Mendes, R. (2006). Neighborhood topologies in fully informed and best-of-neighborhood paper swarms. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(4), 515–519. https://doi.org/10.1109/TSMCC.2006.875410
  • Li, W., Li, S., Chen, Z., Zhong, L., & Ouyang, C. (2019). Self-feedback differential evolution adapting to fitness landscape characteristics. Soft Computing, 23(4), 1151–1163. https://doi.org/10.1007/s00500-017-2833-y
  • Li, W., Meng, X., Huang, Y., & Fu, Z. (2020). Multipopulation cooperative ppaper swarm optimization with a mixed mutation strategy. Information Sciences, 529, 179–196. https://doi.org/10.1016/j.ins.2020.02.034
  • Li, W., Meng, X., & Huang, Y. (2021). Fitness distance correlation and mixed search strategy for differential evolution. Neurocomputing, 458, 514–525. https://doi.org/10.1016/j.neucom.2019.12.141
  • Lin, H., Wang, Y., Gao, Y., & Wang, X. (2018). A filled function method for global optimization with inequality constraints. Computational and Applied Mathematics, 37(2), 1524–1536. https://doi.org/10.1007/s40314-016-0407-8
  • Liu, J., & Lampinen, J. (2005). A fuzzy adaptive differential evolution algorithm. Soft Computing, 9(6), 448–462. https://doi.org/10.1007/s00500-004-0363-x
  • Liu, H., Wang, Y., & Cheung, Y. (2009). A multi-objective evolutionary algorithm using min-max strategy and sphere coordinate transformation. Intelligent Automation and Soft Computing, 15(3), 361–384. https://doi.org/10.1080/10798587.2009.10643036
  • Liu, H. L., Gu, F., & Cheung, Y. (2010). T-MOEA/D: MOEA/D with objective transform in multi-objective problems. In 2010 international conference of information science and management engineering, Vol. 2 (pp. 282–285). IEEE.
  • Liu, H. L., Gu, F., Cheung, Y. M., Xie, S., & Zhang, J. (2014). On solving WCDMA network planning using iterative power control scheme and evolutionary multiobjective algorithm. IEEE Computational Intelligence Magazine, 9(1), 44–52. https://doi.org/10.1109/MCI.2013.2291690
  • Liu, H., Wang, Y., Liu, L., & Li, X. (2018). A two phase hybrid algorithm with a new decomposition method for large scale optimization. Integrated Computer-Aided Engineering, 25(4), 349–367. https://doi.org/10.3233/ICA-170571
  • Liu, J., Wang, Y., Fan, N., Wei, S., & Tong, W. (2019). A convergence-diversity balanced fitness evaluation mechanism for decomposition-based many-objective optimization algorithm. Integrated Computer-Aided Engineering, 26(2), 159–184. https://doi.org/10.3233/ICA-180594
  • Liu, H., Wang, Y., & Fan, N. (2020a). A hybrid deep grouping algorithm for large scale global optimization. IEEE Transactions on Evolutionary Computation, 24(6), 1112–1124. https://doi.org/10.1109/TEVC.4235
  • Liu, J., Wang, Y., Wei, S., Sui, X., & Tong, W. (2020b). A filled flatten function method based on basin deepening and adaptive initial point for global optimization. International Journal of Pattern Recognition and Artificial Intelligence, 34(4), 2059011. https://doi.org/10.1142/S0218001420590119
  • Montgomery, J., & Chen, S. (2010). An analysis of the operation of differential evolution at high and low crossover rates. In Paper presented at the IEEE congress on evolutionary computation. IEEE.
  • Montgomery, J., & Chen, S. (2012). A simple strategy for maintaining diversity and reducing crowding in differential evolution. In Paper presented at the 2012 IEEE congress on evolutionary computation. IEEE.
  • Mohamed, A. W. (2017). Solving stochastic programming problems using new approach to differential evolution algorithm. Egyptian Informatics Journals, 18(2), 75–86. https://doi.org/10.1016/j.eij.2016.09.002
  • Mohamed, A. W., & Suganthan, P. N. (2018). Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Computing, 22(10), 3215–3235. https://doi.org/10.1007/s00500-017-2777-2
  • Mohamed, A. W., & Mohamed, A. K. (2019a). Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. International Journal of Machine Learning and Cybernetics, 10(2), 253–277. https://doi.org/10.1007/s13042-017-0711-7
  • Mohamed, A. W., Mohamed, A. K., Elfeky, E. Z., & Saleh, M. (2019b). Enhanced directed differential evolution algorithm for solving constrained engineering optimization problems. International Journal of Applied Metaheuristic Computing, 10(1), 1–28. https://doi.org/10.4018/IJAMC
  • Nayak, S. K., Rout, P. K., & Jagadev, A. K. (2019a). Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique. Connection Science, 30(4), 362–387. https://doi.org/10.1080/09540091.2018.1487384
  • Nayak, S. K., Rout, P. K., & Jagadev, A. K. (2019b). Multi-objective clustering: a kernel based approach using differential evolution. Connection Science, 31(3), 294–321. https://doi.org/10.1080/09540091.2019.1603201
  • Pant, M., Ali, M., & Singh, V. P. (2009). Differential evolution using quadratic interpolation for initializing the population. In Paper presented at the 2009 IEEE international advance computing conference. IEEE.
  • Pant, M., Zaheer, H., Garcia-Hernandez, L., & Abraham, A. (2020). Differential evolution: a review of more than two decades of research. Engineering Applications of Artificial Intelligence, 90, 103–479. https://doi.org/10.1016/j.engappai.2020.103479
  • Peng, H., Guo, Z., Deng, C., & Wu, Z. (2018). Enhancing differential evolution with random neighbors based strategy. Journal of Computational Science, 26, 501–511. https://doi.org/10.1016/j.jocs.2017.07.010
  • Qin, A. K., Huang, V. L., & Suganthan, P. N. (2008). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation, 13(2), 398–417. https://doi.org/10.1109/TEVC.2008.927706
  • Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. (2008). Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation, 12(1), 64–79. https://doi.org/10.1109/TEVC.2007.894200
  • Shen, D., & Zhu, L. (2019). Differential evolution with spatially neighbourhood best search in dynamic environment. International Journal of Computational Science and Engineering, 19(1), 104–111. https://doi.org/10.1504/IJCSE.2019.099644
  • Song, J., Li, S., Zhao, Z., & Wu, S. (2013). Research on application of thiessen polygon to spatial distribution characteristics of settlement: a case study of Lingao county. Journal of Hainan Normal University (Natural Science), 2.
  • Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328
  • Sun, G., & Cai, Y. (2017). A novel neighborhood-dependent mutation operator for differential evolution. In Paper presented at the 2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC). IEEE.
  • Tanabe, R., & Fukunaga, A. (2013). Success-history based parameter adaptation for differential evolution. In Paper presented at the 2013 IEEE congress on evolutionary computation. IEEE.
  • Tian, M., & Gao, X. (2019). Differential evolution with neighborhood-based adaptive evolution mechanism for numerical optimization. Information Sciences, 478, 422–448. https://doi.org/10.1016/j.ins.2018.11.021
  • Tomassini, M., Vanneschi, L., Collard, P., & Clergue, M. (2005). A study of fitness distance correlation as a difficulty measure in genetic programming. Evolutionary Computation, 13(2), 213. https://doi.org/10.1162/1063656054088549
  • Tong, H., Huang, C., Liu, J., & Yao, X. (2019). Voronoi-based efficient surrogate-assisted evolutionary algorithm for very expensive problems. In Paper presented at the 2019 IEEE congress on evolutionary computation (CEC). IEEE.
  • Wang, Y., Cai, Z., & Zhang, Q. (2011). Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation, 15(1), 55–66. https://doi.org/10.1109/TEVC.2010.2087271
  • Wang, Y., Liu, H., Wei, F., Zong, T., & Li, X. (2018). Cooperative coevolution with formula-based variable grouping for large-scale global optimization. Evolutionary Computation, 26(4), 569–596. https://doi.org/10.1162/evco_a_00214
  • Wang, F., Li, Y., Zhou, A., & Tang, K. (2019). An estimation of distribution algorithm for mixed-variable newsvendor problems. IEEE Transactions on Evolutionary Computation, 24(3), 479–493. https://doi.org/10.1109/TEVC.2019.2932624
  • Wang, F., Zhang, H., & Zhou, A. (2021a). A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm and Evolutionary Computation, 60, 100808. https://doi.org/10.1016/j.swevo.2020.100808
  • Wang, F., Liao, F., Li, Y., & Wang, H. (2021b). A new prediction strategy for dynamic multi-objective optimization using Gaussian mixture model. Information Sciences, 580, 331–351. https://doi.org/10.1016/j.ins.2021.08.065
  • Wu, G., Shen, X., Li, H., Chen, H., Lin, A., & Suganthan, P. N. (2018). Ensemble of differential evolution variants. Information Sciences, 423, 172–186. https://doi.org/10.1016/j.ins.2017.09.053
  • Xue, X., & Wang, Y. (2015). Using memetic algorithm for instance coreference resolution. IEEE Transactions on Knowledge and Data Engineering, 28(2), 580–591. https://doi.org/10.1109/TKDE.2015.2475755
  • Xiang, W. L., Zhu, N., Ma, S. F., Meng, X. L., & An, M. Q. (2015). A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing, 158, 144–154. https://doi.org/10.1016/j.neucom.2015.01.058
  • Zhan, Z., Wang, Z., Jin, H., & Zhang, J. (2019). Adaptive distributed differential evolution. IEEE Transactions on Cybernetics, 50(11), 4633–4647. https://doi.org/10.1109/TCYB.6221036
  • Zhao, Z., Yang, J., Hu, Z., & Che, H. (2016). A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems. European Journal of Operational Research, 250(1), 30–45. https://doi.org/10.1016/j.ejor.2015.10.043
  • Zhao, M., & Cai, Y. (2018). Intelligent selection of parents for mutation in differential evolution. International Journal of Computational Science and Engineering, 17(2), 133–145. https://doi.org/10.1504/IJCSE.2018.094924
  • Zhao, F., Du, S., Lu, H., Ma, W., & Song, H. (2021). A hybrid self-adaptive invasive weed algorithm with differential evolution. Connection Science, 1–25. https://doi.org/10.1080/09540091.2021.1917517
  • Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13(5), 945–958. https://doi.org/10.1109/TEVC.2009.2014613