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An adaptive regeneration framework based on search space adjustment for differential evolution

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Abstract

Differential evolution (DE) is a well-known evolutionary algorithm with simple operation and excellent performance, which has been applied to solve various optimization problems. To alleviate the problem of premature convergence or population stagnation faced by DE algorithm, this paper proposes an adaptive regeneration framework based on search space adjustment (ARSA), which can be easily embedded into various DE variants. When one individual cannot get improved for several generations, the ARSA framework will be triggered to randomly generate a substitute individual from a dynamic search space which is determined by a given adjustment mechanism controlled by two different levels of parameters. The trigger condition for each individual is adaptively controlled by its status in the current population. The space adjustment mechanism contains two strategies, one focuses on global exploration while the other on local exploitation. Moreover, the ARSA framework does not add any parameters that need to be pre-set, and all the included parameters are adaptive. To verify the availability of ARSA framework for solving complex optimization problems, thirty functions with different dimensions from IEEE CEC 2017 test platform and three real-life problems are employed for comparative experiments. The experimental results indicate that our ARSA framework notably improves the performance of two basic DE algorithms and six state-of-the-art DE variants.

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References

  1. Deng W, Xu J, Song Y, Zhao H (2020) Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem. Appl Soft Comput 106724

  2. Machado JT, Pahnehkolaei SMA, Alfi A (2021) Complex-order particle swarm optimization. Commun Nonlinear Sci Numer Simul 92:105448

    Article  MathSciNet  MATH  Google Scholar 

  3. Sun G, Zhao R, Lan Y (2016) Joint operations algorithm for large-scale global optimization. Appl Soft Comput 38:1025–1039

    Article  Google Scholar 

  4. Zhao F, Qin S, Zhang Y, Ma W, Zhang C, Song H (2019) A hybrid biogeography-based optimization with variable neighborhood search mechanism for no-wait flow shop scheduling problem. Expert Syst Appl 126:321–339

    Article  Google Scholar 

  5. Mousavi Y, Alfi A, Kucukdemiral IB (2020) Enhanced fractional chaotic whale optimization algorithm for parameter identification of isolated wind-diesel power systems. IEEE Access 8:140862–140875

    Article  Google Scholar 

  6. Sun G, Lan Y, Zhao R (2019) Self-organizing hierarchical monkey algorithm with time-varying parameter. Neural Comput Appl 31:3245–3263

    Article  Google Scholar 

  7. Zhang Y, Jin Z (2020) Group teaching optimization algorithm: a novel metaheuristic method for solving global optimization problems. Expert Syst Appl 148:113246

    Article  Google Scholar 

  8. Pourpanah F, Shi Y, Lim CP, Hao Q, Tan CJ (2019) Feature selection based on brain storm optimization for data classification. Appl Soft Comput 80:761–775

    Article  Google Scholar 

  9. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  10. Zhu T, Hao Y, Luo W, Ning H (2018) Learning enhanced differential evolution for tracking optimal decisions in dynamic power systems. Appl Soft Comput 67:812–821

    Article  Google Scholar 

  11. Yi W, Zhou Y, Gao L, Li X, Zhang C (2018) Engineering design optimization using an improved local search based epsilon differential evolution algorithm. J Intell Manufact 29:1559–1580

    Article  Google Scholar 

  12. Zhao W, Liu E, Wang B, Gao S, Png CE (2018) Differential evolutionary optimization of an equivalent dipole model for electromagnetic emission analysis. IEEE Trans Electromagn Compat 60(6):1635–1639

    Article  Google Scholar 

  13. Liang J, Wang P, Guo L, Qu B, Yue C, Yu K, Wang Y (2019) Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution. Memet Comput 11:407–422

    Article  Google Scholar 

  14. Wu X, Liu X, Zhao N (2019) An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem. Memet Comput 11(4):335–355

    Article  Google Scholar 

  15. Qiao J, Hou Y, Han H (2019) Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm. Neural Comput Appl 31(7):2537–2550

    Article  Google Scholar 

  16. Atta S, Sen G (2020) Multiple allocation p-hub location problem for content placement in VoD services: a differential evolution based approach. Appl Intell, 1–17

  17. Zhou B, Tan F (2020) A self-adaptive estimation of distribution algorithm with differential evolution strategy for supermarket location problem. Neural Comput Appl 32(10):5791–5804

    Article  Google Scholar 

  18. Al-Dabbagh RD, Neri F, Idris N, Baba MS (2018) Algorithmic design issues in adaptive differential evolution schemes: review and taxonomy. Swarm Evolut Comput 43:284–311

    Article  Google Scholar 

  19. Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13(5):945–958

    Article  Google Scholar 

  20. Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evolut Comput 15(1):55–66

    Article  Google Scholar 

  21. Sun G, Lan Y, Zhao R (2019b) Differential evolution with Gaussian mutation and dynamic parameter adjustment. Soft Comput 23(19):1615–1642

    Article  Google Scholar 

  22. 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 Trans Evolut Comput 10(6):646–657

    Article  Google Scholar 

  23. Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: IEEE congress on evolutionary computation, pp 71–78

  24. Draa A, Bouzoubia S, Boukhalfa I (2015) A sinusoidal differential evolution algorithm for numerical optimisation. Appl Soft Comput 27:99–126

    Article  Google Scholar 

  25. Du W, Leung SYS, Tang Y, Vasilakos AV (2017) Differential evolution with event-triggered impulsive control. IEEE Trans Cybern 47(1):244–257

    Article  Google Scholar 

  26. Guo S, Yang C, Hsu P, Tsai JS (2015) Improving differential evolution with a successful-parent-selecting framework. IEEE Trans Evolut Comput 19(5):717–730

    Article  Google Scholar 

  27. Deng L, Li C, Sun G (2020b) An adaptive dimension level adjustment framework for differential evolution. Knowl Based Syst 206:106388

    Article  Google Scholar 

  28. Bilal, Pant M, Zaheer H, Garcia-Hernandez L Abraham A (2020) Differential evolutio: a review of more than two decades of research. Eng Appl Artif Intell 90:103479

    Article  Google Scholar 

  29. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696

    Article  Google Scholar 

  30. Tanabe R, Fukunaga A (2020) Reviewing and benchmarking parameter control methods in differential evolution. IEEE Trans Cybern 50(3):1170–1184

    Article  Google Scholar 

  31. Draa A, Chettah K, Talbi H (2019) A compound Sinusoidal differential evolution algorithm for continuous optimization. Swarm Evolut Comput 50:100450

    Article  Google Scholar 

  32. Yu W, Shen M, Chen W, Zhan Z, Gong Y, Lin Y, Liu O, Zhang J (2014) Differential evolution with two-level parameter adaptation. IEEE Trans Cybern 44(7):1080–1099

    Article  Google Scholar 

  33. Mohamed AW, Suganthan PN (2018) Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput 22(10):3215–3235

    Article  Google Scholar 

  34. Tatsis VA, Parsopoulos KE (2017) Differential evolution with grid-based parameter adaptation. Soft Comput 21(8):2105–2127

    Article  Google Scholar 

  35. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417

    Article  Google Scholar 

  36. Liu X, Zhan Z, Lin Y, Chen W, Gong Y, Gu T, Yuan H, Zhang J (2019) Historical and heuristic-based adaptive differential evolution. IEEE Trans Syst Man Cybern Syst 49(12):2623–2635

    Article  Google Scholar 

  37. Sun G, Xu G, Jiang N (2020a) A simple differential evolution with time-varying strategy for continuous optimization. Soft Comput 24(4):2727–2747

    Article  Google Scholar 

  38. Gao S, Yu Y, Wang Y, Wang J, Cheng J, Zhou M (2019) Chaotic local search-based differential evolution algorithms for optimization. IEEE Trans Syst Man Cybern Syst, 1–14

  39. Kumar P, Pant M (2012) Enhanced mutation strategy for differential evolution. In: IEEE congress on evolutionary computation, pp 1–6

  40. Gong W, Cai Z (2013) Differential evolution with ranking-based mutation operators. IEEE Trans Cybern 43(6):2066–2081

    Article  Google Scholar 

  41. Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345

    Article  Google Scholar 

  42. Wang B, Li H, Li J, Wang Y (2019) Composite differential evolution for constrained evolutionary optimization. IEEE Trans Syst Man Cybern Syst 49(7):1482–1495

    Article  Google Scholar 

  43. Sengupta R, Pal M, Saha S, Bandyopadhyay S (2020) Uniform distribution driven adaptive differential evolution. Appl Intell 1–22

  44. Sun G, Yang B, Yang Z, Xu G (2020) An adaptive differential evolution with combined strategy for global numerical optimization. Soft Comput 24(9):6277–6296

    Article  Google Scholar 

  45. Tian M, Gao X (2019) An improved differential evolution with information intercrossing and sharing mechanism for numerical optimization. Swarm Evolut Comput 50:100341

    Article  Google Scholar 

  46. Zhan Z, Wang Z, Jin H, Zhang J (2019) Adaptive distributed differential evolution. IEEE Trans Cybern 1–15

  47. Hendtlass T (2001) A combined swarm differential evolution algorithm for optimization problems. In: International conference on industrial, engineering and other applications of applied intelligent systems, pp 11–18

  48. Tang B, Xiang K, Pang M (2020) An integrated particle swarm optimization approach hybridizing a new self-adaptive particle swarm optimization with a modified differential evolution. Neural Comput Appl 32(9):4849–4883

    Article  Google Scholar 

  49. Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput 62:1019–1043

    Article  Google Scholar 

  50. Myszkowski PB, Olech ŁP, Laszczyk M, Skowroński ME (2018) Hybrid differential evolution and greedy algorithm (DEGR) for solving multi-skill resource-constrained project scheduling problem. Appl Soft Comput 62:1–14

    Article  Google Scholar 

  51. Luo J, Shi B (2019) A hybrid whale optimization algorithm based on modified differential evolution for global optimization problems. Appl Intell 49(5):1982–2000

    Article  Google Scholar 

  52. Aguitoni MC, Pavão LV, Ravagnani MA (2019) Heat exchanger network synthesis combining simulated annealing and differential evolution. Energy 181:654–664

    Article  Google Scholar 

  53. Huang Q, Zhang K, Song J, Zhang Y, Shi J (2019) Adaptive differential evolution with a Lagrange interpolation argument algorithm. Inf Sci 472:180–202

    Article  MathSciNet  MATH  Google Scholar 

  54. Guo S, Yang C (2015) Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Trans Evolut Comput 19(1):31–49

    Article  MathSciNet  Google Scholar 

  55. Deng L, Wang S, Qiao L, Zhang B (2018) DE-RCO: rotating crossover operator with multiangle searching strategy for adaptive differential evolution. IEEE Access 6:2970–2983

    Article  Google Scholar 

  56. Deng L, Zhang L, Fu N, Sun H, Qiao L (2020) ERG-DE: an elites regeneration framework for differential evolution. Inf Sci 539:81–103

    Article  MathSciNet  Google Scholar 

  57. Wu G, Shen X, Li H, Chen H, Lin A, Suganthan PN (2018) Ensemble of differential evolution variants. Inf Sci 423:172–186

    Article  MathSciNet  Google Scholar 

  58. Leon M, Xiong N, Molina D, Herrera F (2019) A novel memetic framework for enhancing differential evolution algorithms via combination with Alopex local search. Int J Comput Intell Syst 12(2):795–808

    Article  Google Scholar 

  59. Civicioglu P, Besdok E, Gunen MA, Atasever UH (2020) Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms. Neural Comput Appl 32(8):3923–3937

    Article  Google Scholar 

  60. Wu G, Mallipeddi R, Suganthan PN (2017) Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization, National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report

  61. Herrera F, Lozano M (2000) Gradual distributed real-coded genetic algorithms. IEEE Trans Evolut Comput 4(1):43–63

    Article  Google Scholar 

  62. Das S, Suganthan PN (2011) Problem definitions, and evaluation criteria for CEC competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Nanyang Technological University, Kolkata 2010, pp 341–359

  63. Hui W, Rahnamayan S, Hui S, Omran MGH (2013) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43(2):634–647

    Article  Google Scholar 

  64. Zhou Y, Yi W, Gao L, Li X (2017) Adaptive differential evolution with sorting crossover rate for continuous optimization problems. IEEE Trans Cybern 47(9):2742–2753

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 71701187, 71704162 and 61401121, in part by the Fundamental Research Funds for the Central Universities under Grant HIT NSRIF 2019083, in part by the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments under Grant YQ19203.

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  1. College of Economic and Management, Zhejiang Normal University, Jinhua, 321004, China

    Gaoji Sun

  2. School of Information Science and Engineering, Harbin Institute of Technology, Weihai, 264209, China

    Chunlei Li & Libao Deng

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  1. Gaoji Sun
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  2. Chunlei Li
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Correspondence to Libao Deng.

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Sun, G., Li, C. & Deng, L. An adaptive regeneration framework based on search space adjustment for differential evolution. Neural Comput & Applic 33, 9503–9519 (2021). https://doi.org/10.1007/s00521-021-05708-1

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