【差分演化算法相关文献总结】

差分演化算法相关文献总结

  • 前言
  • 概述
  • 文献综述
  • 总结

前言

  本人作为一名从事了三年演化算法研究的菜鸡研究生,其中大部分时间都在专注于差分演化算法(Differential Evolution, DE)的相关研究。现如今已经毕业,回顾往昔,经过阅读大量的文献,也算是浅浅的入了演化算法的门。
  本文将总结出我在读研期间所收集和阅读过的与 DE 相关的一些论文,以供从事演化算法研究,尤其是 DE 算法研究的各位学者们进行学习和参考。下面我会附上论文的名称对应的连接,感兴趣的小伙伴可自行下载阅读。
  首先,我们还是先附上 DE 原文:
Storn R, Price K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of global optimization, 1997, 11: 341-359.

概述

  在众多的演化算法中,差分演化算法作为一种经典高效元启发算法,具有参数少收敛快鲁棒性高等优点,使其一度成为了演化计算领域的热点。在2006年至2009年由IEEE举办的CEC演化大赛中,DE连续取得了第一的名次,并且在近三年的竞赛中,DE依旧具有更好的竞争力。DE是由Storn1995所提出的一种具有较强鲁棒性的优化算法,通过基于种群的随机搜索方式来进行演化更新,每一代的个体都会经历突变交叉选择操作,从而将种群不断向全局最优引导。由于其具有较强的鲁棒性简单性DE进化已成功地应用于医疗问题优化工程设计路径规划计算机视觉等各种领域中,并取得了显著的效果。
  虽然DE存在许多的优点且受到了广泛的使用,但是该算法依旧存在较多的问题和提升空间,如较多的参数设置演化寻优的随机性较大种群多样性的丧失算法易陷入局部最优算法早熟搜索停滞等现象。此类问题的出现会降低DE的算法性能,在解决实际优化问题时会造成不同程度的影响。所以,受到实际问题的驱动,对DE算法的改进和优化从未停止,众多研究者对DE存在的问题进行了讨论及优化。现如今,已有大量的DE变体被提出,极大程度的提高了算法的性能。

在这里插入图片描述

文献综述

  1. Brest J, Greiner S, Boskovic B, et al. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems[J]. IEEE transactions on evolutionary computation, 2006, 10(6): 646-657.
  2. Qin A K, Huang V L, Suganthan P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE transactions on Evolutionary Computation, 2008, 13(2): 398-417.
  3. Fan Q, Wang W, Yan X. Differential evolution algorithm with strategy adaptation and knowledge-based control parameters[J]. Artificial Intelligence Review, 2019, 51: 219-253.
  4. Wang Y, Cai Z, Zhang Q. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE transactions on evolutionary computation, 2011, 15(1): 55-66.
  5. Zhang J, Sanderson A C. JADE: adaptive differential evolution with optional external archive[J]. IEEE Transactions on evolutionary computation, 2009, 13(5): 945-958.
  6. Mallipeddi R, Suganthan P N, Pan Q K, et al. Differential evolution algorithm with ensemble of parameters and mutation strategies[J]. Applied soft computing, 2011, 11(2): 1679-1696.
  7. Mallipeddi R, Suganthan P N. Differential evolution algorithm with ensemble of parameters and mutation and crossover strategies[C]//Swarm, Evolutionary, and Memetic Computing: First International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2010, Chennai, India, December 16-18, 2010. Proceedings 1. Springer Berlin Heidelberg, 2010: 71-78.
  8. Wu G, Mallipeddi R, Suganthan P N, et al. Differential evolution with multi-population based ensemble of mutation strategies[J]. Information Sciences, 2016, 329: 329-345.
  9. Tanabe R, Fukunaga A. Success-history based parameter adaptation for differential evolution[C]//2013 IEEE congress on evolutionary computation. IEEE, 2013: 71-78.
  10. Li X, Wang L, Jiang Q, et al. Differential evolution algorithm with multi-population cooperation and multi-strategy integration[J]. Neurocomputing, 2021, 421: 285-302.
  11. Das S, Mullick S S, Suganthan P N. Recent advances in differential evolution–an updated survey[J]. Swarm and evolutionary computation, 2016, 27: 1-30.
  12. Hassan S, Hemeida A M, Alkhalaf S, et al. Multi-variant differential evolution algorithm for feature selection[J]. Scientific Reports, 2020, 10(1): 17261.
  13. Ahandani M A. Opposition-based learning in the shuffled bidirectional differential evolution algorithm[J]. Swarm and Evolutionary Computation, 2016, 26: 64-85.
  14. Liu X F, Zhan Z H, Lin Y, et al. Historical and heuristic-based adaptive differential evolution[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 49(12): 2623-2635.
  15. Ortiz M L, Xiong N. Using random local search helps in avoiding local optimum in differential evolution[C]//Proc. IASTED. 2014: 413-420.
  16. Fan Q, Yan X. Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies[J]. IEEE transactions on cybernetics, 2015, 46(1): 219-232.
  17. Tian M, Gao X, Dai C. Differential evolution with improved individual-based parameter setting and selection strategy[J]. Applied Soft Computing, 2017, 56: 286-297.
  18. Gong W, Cai Z, Liang D. Adaptive ranking mutation operator based differential evolution for constrained optimization[J]. IEEE transactions on cybernetics, 2014, 45(4): 716-727.
  19. Tanabe R, Fukunaga A S. Improving the search performance of SHADE using linear population size reduction[C]//2014 IEEE congress on evolutionary computation (CEC). IEEE, 2014: 1658-1665.
  20. ZDT D T. Performance analysis of variants of differential evolution on multi-objective optimization problems[J]. Indian Journal of Science and Technology, 2015, 8(17): 65727.
  21. Peng H, Guo Z, Deng C, et al. Enhancing differential evolution with random neighbors based strategy[J]. Journal of Computational Science, 2018, 26: 501-511.
  22. Yu W J, Shen M, Chen W N, et al. Differential evolution with two-level parameter adaptation[J]. IEEE Transactions on Cybernetics, 2013, 44(7): 1080-1099.
  23. Wang Y, Li H X, Huang T, et al. Differential evolution based on covariance matrix learning and bimodal distribution parameter setting[J]. Applied Soft Computing, 2014, 18: 232-247.
  24. Xia X, Tong L, Zhang Y, et al. NFDDE: A novelty-hybrid-fitness driving differential evolution algorithm[J]. Information Sciences, 2021, 579: 33-54.
  25. Brest J. Constrained real-parameter optimization with ε-self-adaptive differential evolution[M]. Springer Berlin Heidelberg, 2009.
  26. Huynh T N, Do D T T, Lee J. Q-Learning-based parameter control in differential evolution for structural optimization[J]. Applied Soft Computing, 2021, 107: 107464.
  27. Meng Z, Yang C. Hip-DE: Historical population based mutation strategy in differential evolution with parameter adaptive mechanism[J]. Information Sciences, 2021, 562: 44-77.
  28. Li S, Gu Q, Gong W, et al. An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models[J]. Energy Conversion and Management, 2020, 205: 112443.
  29. Hao Q, Zhou Z, Wei Z, et al. Parameters identification of photovoltaic models using a multi-strategy success-history-based adaptive differential evolution[J]. IEEE Access, 2020, 8: 35979-35994.
  30. Huang Q, Zhang K, Song J, et al. Adaptive differential evolution with a Lagrange interpolation argument algorithm[J]. Information Sciences, 2019, 472: 180-202.
  31. Gong W, Cai Z, Ling C X, et al. Enhanced differential evolution with adaptive strategies for numerical optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 41(2): 397-413.
  32. Qian W, Chai J, Xu Z, et al. Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection[J]. Applied Intelligence, 2018, 48: 3612-3629.
  33. Liu Z Z, Wang Y, Yang S, et al. Differential evolution with a two-stage optimization mechanism for numerical optimization[C]//2016 IEEE congress on evolutionary computation (CEC). IEEE, 2016: 3170-3177.
  34. Wang Y, Yu J, Yang S, et al. Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons[J]. Swarm and Evolutionary Computation, 2019, 50: 100559.
  35. Li Y, Wang S, Liu H, et al. A backtracking differential evolution with multi-mutation strategies autonomy and collaboration[J]. Applied Intelligence, 2022: 1-27.
  36. Ali I M, Essam D, Kasmarik K. Novel binary differential evolution algorithm for knapsack problems[J]. Information Sciences, 2021, 542: 177-194.
  37. Xie W, Yu W, Zou X. Diversity-maintained differential evolution embedded with gradient-based local search[J]. Soft computing, 2013, 17: 1511-1535.
  38. Peng H, Wu Z. Heterozygous differential evolution with Taguchi local search[J]. Soft Computing, 2015, 19: 3273-3291.
  39. Liao J, Cai Y, Wang T, et al. Cellular direction information based differential evolution for numerical optimization: an empirical study[J]. Soft Computing, 2016, 20: 2801-2827.
  40. Kaelo P, Ali M M. A numerical study of some modified differential evolution algorithms[J]. European journal of operational research, 2006, 169(3): 1176-1184.
  41. Brest J, Maučec M S, Bošković B. Single objective real-parameter optimization: Algorithm jSO[C]//2017 IEEE congress on evolutionary computation (CEC). IEEE, 2017: 1311-1318.
  42. Tan Z, Li K, Wang Y. Differential evolution with adaptive mutation strategy based on fitness landscape analysis[J]. Information Sciences, 2021, 549: 142-163.
  43. Zuo Y, Zhao F, Li Z. A knowledge-based differential covariance matrix adaptation cooperative algorithm[J]. Expert Systems with Applications, 2021, 184: 115495.
  44. Zeng Z, Zhang M, Chen T, et al. A new selection operator for differential evolution algorithm[J]. Knowledge-Based Systems, 2021, 226: 107150.
  45. Lu Z, Zhang L, Wang D. Differential evolution with improved elite archive mutation and dynamic parameter adjustment[J]. Cluster Computing, 2019, 22: 9347-9356.
  46. Zhang X, Zhang X. Improving differential evolution by differential vector archive and hybrid repair method for global optimization[J]. Soft Computing, 2017, 21: 7107-7116.
  47. Das S, Konar A, Chakraborty U K. Two improved differential evolution schemes for faster global search[C]//Proceedings of the 7th annual conference on Genetic and evolutionary computation. 2005: 991-998.
  48. Yang Z, Yao X, He J. Making a difference to differential evolution[J]. Advances in metaheuristics for hard optimization, 2008: 397-414.
  49. Das S, Konar A, Chakraborty U K. Two improved differential evolution schemes for faster global search[C]//Proceedings of the 7th annual conference on Genetic and evolutionary computation. 2005: 991-998.
  50. Liang J, Qiao K, Yu K, et al. Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution[J]. Solar Energy, 2020, 207: 336-346.
  51. Cui L, Huang Q, Li G, et al. Differential evolution algorithm with tracking mechanism and backtracking mechanism[J]. IEEE Access, 2018, 6: 44252-44267.
  52. Meng Z, Chen Y, Li X. Enhancing differential evolution with novel parameter control[J]. IEEE Access, 2020, 8: 51145-51167.
  53. Zou D, Gong D. Differential evolution based on migrating variables for the combined heat and power dynamic economic dispatch[J]. Energy, 2022, 238: 121664.
  54. Zhou X G, Zhang G J, Hao X H, et al. Differential evolution with multi-stage strategies for global optimization[C]//2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016: 2550-2557.
  55. Mohamed A K, Mohamed A W. Real-parameter unconstrained optimization based on enhanced AGDE algorithm[J]. Machine learning paradigms: Theory and application, 2019: 431-450.
  56. Cui L, Li G, Zhu Z, et al. Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism[J]. Information Sciences, 2018, 422: 122-143.
  57. Kizilay D, Tasgetiren M F, Oztop H, et al. A differential evolution algorithm with q-learning for solving engineering design problems[C]//2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020: 1-8.
  58. Sharma M, Komninos A, López-Ibáñez M, et al. Deep reinforcement learning based parameter control in differential evolution[C]//Proceedings of the Genetic and Evolutionary Computation Conference. 2019: 709-717.
  59. Tan Z, Li K. Differential evolution with mixed mutation strategy based on deep reinforcement learning[J]. Applied Soft Computing, 2021, 111: 107678.
  60. Zhang H, Sun J, Xu Z. Learning to mutate for differential evolution[C]//2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021: 1-8.
  61. El-Qulity S A, Mohamed A W. A generalized national planning approach for admission capacity in higher education: a nonlinear integer goal programming model with a novel differential evolution algorithm[J]. Computational Intelligence and Neuroscience, 2016, 2016: 21-21.
  62. Mohamed A W. An improved differential evolution algorithm with triangular mutation for global numerical optimization[J]. Computers & Industrial Engineering, 2015, 85: 359-375.
  63. Mohamed A W, Mohamed A K. Adaptive guided differential evolution algorithm with novel mutation for numerical optimization[J]. International Journal of Machine Learning and Cybernetics, 2019, 10: 253-277.
  64. Wu G, Shen X, Li H, et al. Ensemble of differential evolution variants[J]. Information Sciences, 2018, 423: 172-186.
  65. Mohamed A W, Hadi A A, Jambi K M. Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization[J]. Swarm and Evolutionary Computation, 2019, 50: 100455.
  66. Mohamed A W, Hadi A A, Mohamed A K. Differential evolution mutations: taxonomy, comparison and convergence analysis[J]. IEEE Access, 2021, 9: 68629-68662.
  67. Mohamed A W, Hadi A A, Fattouh A M, et al. LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems[C]//2017 IEEE Congress on evolutionary computation (CEC). IEEE, 2017: 145-152.
  68. Kumar A, Misra R K, Singh D. Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase[C]//2017 IEEE congress on evolutionary computation (CEC). IEEE, 2017: 1835-1842.
  69. Kudela J, Matousek R. Lipschitz-based surrogate model for high-dimensional computationally expensive problems[J]. arXiv preprint arXiv:2204.14236, 2022.
  70. Cui L, Huang Q, Li G, et al. Differential evolution algorithm with tracking mechanism and backtracking mechanism[J]. IEEE Access, 2018, 6: 44252-44267.
  71. Meng Z, Zhong Y, Yang C. CS-DE: Cooperative strategy based differential evolution with population diversity enhancement[J]. Information Sciences, 2021, 577: 663-696.
  72. Meng Z, Yang C. Hip-DE: Historical population based mutation strategy in differential evolution with parameter adaptive mechanism[J]. Information Sciences, 2021, 562: 44-77.
  73. Meng Z, Yang C, Li X, et al. Di-DE: depth information-based differential evolution with adaptive parameter control for numerical optimization[J]. IEEE Access, 2020, 8: 40809-40827.
  74. Biswas S, Saha D, De S, et al. Improving differential evolution through Bayesian hyperparameter optimization[C]//2021 IEEE Congress on evolutionary computation (CEC). IEEE, 2021: 832-840.
  75. Cui L, Li G, Lin Q, et al. Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations[J]. Computers & Operations Research, 2016, 67: 155-173.
  76. Ge Y F, Yu W J, Lin Y, et al. Distributed differential evolution based on adaptive mergence and split for large-scale optimization[J]. IEEE transactions on cybernetics, 2017, 48(7): 2166-2180.
  77. Tan Z, Tang Y, Li K, et al. Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning[J]. Swarm and Evolutionary Computation, 2022, 75: 101194.
  78. Gui L, Xia X, Yu F, et al. A multi-role based differential evolution[J]. Swarm and Evolutionary Computation, 2019, 50: 100508.
  79. Cao Z, Jia H, Wang Z, et al. A differential evolution with autonomous strategy selection and its application in remote sensing image denoising[J]. Expert Systems with Applications, 2023: 122108.
  80. Li C, Sun G, Deng L, et al. A population state evaluation-based improvement framework for differential evolution[J]. Information Sciences, 2023, 629: 15-38.
  81. Li Y, Wang S, Yang B, et al. Population reduction with individual similarity for differential evolution[J]. Artificial Intelligence Review, 2023, 56(5): 3887-3949.
  82. Ahmad M F, Isa N A M, Lim W H, et al. Differential evolution: A recent review based on state-of-the-art works[J]. Alexandria Engineering Journal, 2022, 61(5): 3831-3872.
  83. Song Y, Cai X, Zhou X, et al. Dynamic hybrid mechanism-based differential evolution algorithm and its application[J]. Expert Systems with Applications, 2023, 213: 118834.
  84. Wang M, Ma Y, Wang P. Parameter and strategy adaptive differential evolution algorithm based on accompanying evolution[J]. Information Sciences, 2022, 607: 1136-1157.
  85. Meng Z, Yang C. Two-stage differential evolution with novel parameter control[J]. Information Sciences, 2022, 596: 321-342.
  86. Piotrowski A P, Napiorkowski J J, Piotrowska A E. Particle swarm optimization or differential evolution—A comparison[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 106008.
  87. Zhang S X, Wen Y N, Liu Y H, et al. Differential evolution with domain transform[J]. IEEE Transactions on Evolutionary Computation, 2022.
  88. Qiao K, Liang J, Yu K, et al. Self-adaptive resources allocation-based differential evolution for constrained evolutionary optimization[J]. Knowledge-Based Systems, 2022, 235: 107653.
  89. Qiao K, Liang J, Qu B, et al. Differential evolution with level-based learning mechanism[J]. Complex System Modeling and Simulation, 2022, 2(1): 35-58.
  90. Cao Z, Wang Z, Fu Y, et al. An adaptive differential evolution framework based on population feature information[J]. Information Sciences, 2022, 608: 1416-1440.
  91. Song Y, Zhao G, Zhang B, et al. An enhanced distributed differential evolution algorithm for portfolio optimization problems[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 106004.
  92. Li Y, Han T, Tang S, et al. An improved differential evolution by hybridizing with estimation-of-distribution algorithm[J]. Information Sciences, 2023, 619: 439-456.
  93. Zeng Z, Zhang M, Hong Z, et al. Enhancing differential evolution with a target vector replacement strategy[J]. Computer Standards & Interfaces, 2022, 82: 103631.
  94. Zeng Z, Hong Z, Zhang H, et al. Improving differential evolution using a best discarded vector selection strategy[J]. Information Sciences, 2022, 609: 353-375.
  95. Vermetten D, van Stein B, Kononova A V, et al. Analysis of structural bias in differential evolution configurations[M]//Differential Evolution: From Theory to Practice. Singapore: Springer Nature Singapore, 2022: 1-22.
  96. Kitamura T, Fukunaga A. Differential Evolution with an Unbounded Population[C]//2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2022: 1-8.
  97. Gupta S, Su R. An efficient differential evolution with fitness-based dynamic mutation strategy and control parameters[J]. Knowledge-Based Systems, 2022, 251: 109280.
  98. Deng W, Ni H, Liu Y, et al. An adaptive differential evolution algorithm based on belief space and generalized opposition-based learning for resource allocation[J]. Applied Soft Computing, 2022, 127: 109419.
  99. Li Y, Wang S, Yang H, et al. Enhancing differential evolution algorithm using leader-adjoint populations[J]. Information Sciences, 2023, 622: 235-268.
  100. Chen J, Wang R, Wu D, et al. A differential evolution-enhanced position-transitional approach to latent factor analysis[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, 7(2): 389-401.

总结

  有关 DE 的相关论文不计其数,这里小编也不可能一一的列出。但是希望从事 DE 相关研究的学者能够不断的集思广益,多汲取一些大佬的思想,能够在算法优化上更上一层楼。
  我将收集到的一些论文进行了打包上传,链接如下:差分演化算法相关学术论文集合

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