Differential evolution algorithm with elite archive and mutation strategies collaboration

  • Yuzhen Li
  • Shihao WangEmail author


This paper proposes a differential evolution algorithm with elite archive and mutation strategies collaboration (EASCDE), wherein two main improvements are presented. Firstly, an elite archive mechanism is introduced to make DE/rand/3 and DE/current-to-best/2 mutation strategies converge faster. Secondly, a mutation strategies collaboration mechanism is developed to tightly combine both strategies to balance global exploration and local exploitation. As a result, EASCDE can effectively keep population diversity in the early stage and significantly enhance convergence speed as well as solution quality in the later stage. The performance of EASCDE is verified by experimental analyses on the well-known test functions. The results demonstrate that EASCDE is superior to other compared competitors in terms of solution precision, convergence speed and stability. Moreover, EASCDE is also an efficient method in dealing with arrival flights scheduling problem.


Differential evolution Elite archive mechanism Mutation strategies collaboration mechanism Arrival flights scheduling 



The authors sincerely thank the reviewers for their beneficial suggestions.

Compliance with ethical standards

Conflict of interest

The authors state that there is no conflict of interest.


  1. Awad NH, Ali MZ, Suganthan PN, Reynolds RG (2017) CADE: a hybridization of cultural algorithm and differential evolution for numerical optimization. Inf Sci 378:215–241CrossRefGoogle Scholar
  2. Awad NH, Ali MZ, Suganthan PN (2018) Ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction. Swarm Evol Comput 39:141–156CrossRefGoogle Scholar
  3. Babu BV, Angira R (2006) Modified differential evolution (MDE) for optimization of non-linear chemical processes. Comput Chem Eng 30:989–1002zbMATHCrossRefGoogle Scholar
  4. Brest J, Maucec MS (2008) Population size reduction for the differential evolution algorithm. Appl Intell 29(3):228–247CrossRefGoogle Scholar
  5. 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 Evol Comput 10(6):646–657CrossRefGoogle Scholar
  6. Cui L, Li G, Lin Q et al (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155–173MathSciNetzbMATHCrossRefGoogle Scholar
  7. Dash R, Dash PK, Bisoi R (2014) A self-adaptive differential harmony search based optimized extreme learning machine for financial time series prediction. Swarm Evol Comput 19:25–42CrossRefGoogle Scholar
  8. Ela AAAE, Abido MA, Spea SR (2009) Optimal power flow using differential evolution algorithm. Electr Eng 91(2):69–78CrossRefGoogle Scholar
  9. Elsayed SM, Sarker RA (2013) Differential evolution with automatic population injection scheme for constrained problems. In: IEEE symposium on differential evolution (SDE), IEEE, SingaporeGoogle Scholar
  10. Elsayed S, Sarker R, Essam D (2011) Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems. IEEE Congr Evol Comput, New Orleans, pp 1041–1048Google Scholar
  11. Elsayed SM, Sarker RA, Essam DL (2013) Self-adaptive differential evolution incorporating a heuristic mixing of operators. Comput Optim Appl 54:771–790MathSciNetzbMATHCrossRefGoogle Scholar
  12. Epitropakis MG, Tasoulis DK, Pavlidis NG et al (2011) Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans Evol Comput 15(1):99–119CrossRefGoogle Scholar
  13. Fan Q, Wang W, Yan X (2019) Differential evolution algorithm with strategy adaptation and knowledge-based control parameters. Artif Intell Rev 51(2):219–253CrossRefGoogle Scholar
  14. Gamperle R, Muller SD, Koumoutsakos P (2002) A parameter study for differential evolution. In: WSEAS international conference on advances in intelligent systems, fuzzy systems, evolutionary computation, WSEAS, New York, pp 293–298Google Scholar
  15. Gandomi AH, Yang X, Talatahari S, Deb S (2012) Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput Math Appl 63(1):191–200MathSciNetzbMATHCrossRefGoogle Scholar
  16. Ghosh A, Das S, Chowdhury A, Giri R (2011) An improved differential evolution algorithm with fitness-based adaptation of the control parameters. Inf Sci 181:3749–3765MathSciNetCrossRefGoogle Scholar
  17. Gong W, Cai Z (2013) Differential evolution with ranking-based mutation operators. IEEE Trans Cybern 43(6):2066–2081CrossRefGoogle Scholar
  18. Gong W, Cai Z, Ling C (2011) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15:645–665CrossRefGoogle Scholar
  19. Kok KY, Rajendran P (2016) Differential-evolution control parameter optimization for unmanned aerial vehicle path planning. Plos ONE. CrossRefGoogle Scholar
  20. Li J, Ding L, Xing Y (2013) Differential evolution based parameters selection for support vector machine. In: 9th international conference on computational intelligence and security, IEEE, LeshanGoogle Scholar
  21. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696CrossRefGoogle Scholar
  22. Mao B, Xie Z, Wang Y, Handroos H, Wu H, Shi S (2017) A hybrid differential evolution and particle swarm optimization algorithm for numerical kinematics solution of remote maintenance manipulators. Fus Eng Des 124:587–590CrossRefGoogle Scholar
  23. Melo VV, Delbem ACB (2012) Investigating smart sampling as a population initialization method for differential evolution in continuous problems. Inf Sci 193:36–53CrossRefGoogle Scholar
  24. Mohamed AW, Mohamed AK (2019) Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int J Mach Learn Cybern 10(2):253–257CrossRefGoogle Scholar
  25. Nasimul N, Danushka B, Hitoshi I (2011) An adaptive differential evolution algorithm. In: IEEE congress on evolutionary computation, IEEE, New Orleans, pp 2229–2236Google Scholar
  26. Pan Q, Wang L (2008) A novel differential evolution algorithm for no-idle permutation flow-shop scheduling problems. Eur J Ind Eng 2(3):279–297MathSciNetCrossRefGoogle Scholar
  27. Pan Q, Tasgetiren MF, Liang Y (2008) A discrete differential evolution algorithm for the permutation flowshop scheduling problem. Comput Ind Eng 55:795–816CrossRefGoogle Scholar
  28. Pant M, Aliandv M, Singh VP (2009) Differential evolution using quadratic interpolation for initializing the population. In: IEEE international advance computing conference, IEEE, PatialaGoogle Scholar
  29. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417CrossRefGoogle Scholar
  30. Qu BY, Suganthan PN, Liang JJ (2012) Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans Evol Comput 16(5):601–614CrossRefGoogle Scholar
  31. Ronkkonen J, Kukkonen S, Price KV (2005) Real-parameter optimization with differential evolution. In: IEEE congress on evolutionary computation, pp 506–513Google Scholar
  32. Sarkar S, Das S, Chaudhuri SS (2015) A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recogn Lett 54:27–35CrossRefGoogle Scholar
  33. Storn R, Price KV (1995) Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley, CA, USA, Technology Report. TR-95-012Google Scholar
  34. Storn R, Price KV (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359MathSciNetzbMATHCrossRefGoogle Scholar
  35. Sun G, Xu G, Gao R, Liu J (2019) A fluctuant population strategy for differential evolution. Evol Intel. CrossRefGoogle Scholar
  36. Teo J (2006) Exploring dynamic self-adaptive populations in differential evolution. Soft Comput 10(8):673–686CrossRefGoogle Scholar
  37. Ting C, Huang C (2009) Varying number of difference vectors in differential evolution. In: IEEE congress on evolutionary computation, pp 1351–1358Google Scholar
  38. Trivedi A, Srinivasan D, Biswas S, Reindl T (2015) Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem. Swarm Evol Comput 23:50–64CrossRefGoogle Scholar
  39. Wang L, Li L (2012) A coevolutionary differential evolution with harmony search for reliability-redundancy optimization. Expert Syst Appl 39(5):5271–5278CrossRefGoogle Scholar
  40. Wang Y, Cai Z, Zhang Q (2011a) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66CrossRefGoogle Scholar
  41. Wang H, Rahnamayan S, Wu Z (2011) Adaptive eifferential evolution with variable population size for solving high-dimensional problems. In: IEEE congress of evolutionary computation, IEEE, New Orleans, LAGoogle Scholar
  42. Wang H, Wu Z, Rahnamayan S (2011c) Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft Comput 15(11):2127–2140CrossRefGoogle Scholar
  43. Wang H, Rahnamayan S, Sun H, Omran MG (2013) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43(2):634–647CrossRefGoogle Scholar
  44. Wang G, Gandomi A, Alavi A, Hao G (2014) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 25(2):297–308CrossRefGoogle Scholar
  45. Wang S, Yang H, Wu X, Liu H (2015) The research on optimization mathematical model of arrival flights scheduling. Adv Eng Sci 47(6):113–120MathSciNetGoogle Scholar
  46. Wang S, Li Y, Yang H (2017) Self-adaptive differential evolution algorithm with improved mutation mode. Appl Intell 47:644–658CrossRefGoogle Scholar
  47. Wang S, Li Y, Yang Y, Liu H (2018) Self-adaptive differential evolution algorithm with improved mutation strategy. Soft Comput 22(10):3433–3447CrossRefGoogle Scholar
  48. Wang S, Li Y, Yang H (2019) Self-adaptive mutation differential evolution algorithm based on particle swarm optimization. Appl Soft Comput. CrossRefGoogle Scholar
  49. Yu W, Shen M, Chen W et al (2014) Differential evolution with two-level parameter adaptation. IEEE Trans Cybern 44(7):1080–1099CrossRefGoogle Scholar
  50. Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958CrossRefGoogle Scholar
  51. Zhang WJ, Xie XF (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: IEEE international conference on systems, man and cybernetics, IEEE, Washington, pp 3816–3821Google Scholar
  52. Zhang G, Cheng J, Gheorghe M, Meng Q (2013) A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Appl Soft Comput 13(3):1528–1542CrossRefGoogle Scholar
  53. Zhao Z, Yang J, Hu Z, Chen H (2016) 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 250(1):30–45MathSciNetzbMATHCrossRefGoogle Scholar
  54. Zhou Y, Li X, Gao L (2013) A differential evolution algorithm with intersect mutation operator. Appl Soft Comput 13:390–401CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Information SecurityHenan Police CollegeZhengzhouChina

Personalised recommendations