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Differential evolution algorithm with elite archive and mutation strategies collaboration

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Abstract

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.

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References

  • Awad NH, Ali MZ, Suganthan PN, Reynolds RG (2017) CADE: a hybridization of cultural algorithm and differential evolution for numerical optimization. Inf Sci 378:215–241

    Google Scholar 

  • Awad NH, Ali MZ, Suganthan PN (2018) Ensemble of parameters in a sinusoidal differential evolution with niching-based population reduction. Swarm Evol Comput 39:141–156

    Google Scholar 

  • Babu BV, Angira R (2006) Modified differential evolution (MDE) for optimization of non-linear chemical processes. Comput Chem Eng 30:989–1002

    MATH  Google Scholar 

  • Brest J, Maucec MS (2008) Population size reduction for the differential evolution algorithm. Appl Intell 29(3):228–247

    Google Scholar 

  • 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):646–657

    Google Scholar 

  • Cui L, Li G, Lin Q et al (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155–173

    MathSciNet  MATH  Google Scholar 

  • 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 19:25–42

    Google Scholar 

  • Ela AAAE, Abido MA, Spea SR (2009) Optimal power flow using differential evolution algorithm. Electr Eng 91(2):69–78

    Google Scholar 

  • Elsayed SM, Sarker RA (2013) Differential evolution with automatic population injection scheme for constrained problems. In: IEEE symposium on differential evolution (SDE), IEEE, Singapore

  • 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 1041–1048

    Google Scholar 

  • Elsayed SM, Sarker RA, Essam DL (2013) Self-adaptive differential evolution incorporating a heuristic mixing of operators. Comput Optim Appl 54:771–790

    MathSciNet  MATH  Google Scholar 

  • Epitropakis MG, Tasoulis DK, Pavlidis NG et al (2011) Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans Evol Comput 15(1):99–119

    Google Scholar 

  • Fan Q, Wang W, Yan X (2019) Differential evolution algorithm with strategy adaptation and knowledge-based control parameters. Artif Intell Rev 51(2):219–253

    Google Scholar 

  • 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 293–298

  • 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):191–200

    MathSciNet  MATH  Google Scholar 

  • Ghosh A, Das S, Chowdhury A, Giri R (2011) An improved differential evolution algorithm with fitness-based adaptation of the control parameters. Inf Sci 181:3749–3765

    MathSciNet  Google Scholar 

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

    Google Scholar 

  • Gong W, Cai Z, Ling C (2011) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15:645–665

    Google Scholar 

  • Kok KY, Rajendran P (2016) Differential-evolution control parameter optimization for unmanned aerial vehicle path planning. Plos ONE. https://doi.org/10.1371/journal.pone.0150558

    Article  Google Scholar 

  • 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, Leshan

  • 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

    Google Scholar 

  • 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 124:587–590

    Google Scholar 

  • Melo VV, Delbem ACB (2012) Investigating smart sampling as a population initialization method for differential evolution in continuous problems. Inf Sci 193:36–53

    Google Scholar 

  • Mohamed AW, Mohamed AK (2019) Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int J Mach Learn Cybern 10(2):253–257

    Google Scholar 

  • Nasimul N, Danushka B, Hitoshi I (2011) An adaptive differential evolution algorithm. In: IEEE congress on evolutionary computation, IEEE, New Orleans, pp 2229–2236

  • Pan Q, Wang L (2008) A novel differential evolution algorithm for no-idle permutation flow-shop scheduling problems. Eur J Ind Eng 2(3):279–297

    MathSciNet  Google Scholar 

  • Pan Q, Tasgetiren MF, Liang Y (2008) A discrete differential evolution algorithm for the permutation flowshop scheduling problem. Comput Ind Eng 55:795–816

    Google Scholar 

  • Pant M, Aliandv M, Singh VP (2009) Differential evolution using quadratic interpolation for initializing the population. In: IEEE international advance computing conference, IEEE, Patiala

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

    Google Scholar 

  • Qu BY, Suganthan PN, Liang JJ (2012) Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans Evol Comput 16(5):601–614

    Google Scholar 

  • Ronkkonen J, Kukkonen S, Price KV (2005) Real-parameter optimization with differential evolution. In: IEEE congress on evolutionary computation, pp 506–513

  • Sarkar S, Das S, Chaudhuri SS (2015) A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recogn Lett 54:27–35

    Google Scholar 

  • 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-95-012

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

    MathSciNet  MATH  Google Scholar 

  • Sun G, Xu G, Gao R, Liu J (2019) A fluctuant population strategy for differential evolution. Evol Intel. https://doi.org/10.1007/s12065-019-00287-6

    Article  Google Scholar 

  • Teo J (2006) Exploring dynamic self-adaptive populations in differential evolution. Soft Comput 10(8):673–686

    Google Scholar 

  • Ting C, Huang C (2009) Varying number of difference vectors in differential evolution. In: IEEE congress on evolutionary computation, pp 1351–1358

  • 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 23:50–64

    Google Scholar 

  • Wang L, Li L (2012) A coevolutionary differential evolution with harmony search for reliability-redundancy optimization. Expert Syst Appl 39(5):5271–5278

    Google Scholar 

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

    Google Scholar 

  • 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, LA

  • Wang H, Wu Z, Rahnamayan S (2011c) Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft Comput 15(11):2127–2140

    Google Scholar 

  • Wang H, Rahnamayan S, Sun H, Omran MG (2013) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43(2):634–647

    Google Scholar 

  • 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):297–308

    Google Scholar 

  • Wang S, Yang H, Wu X, Liu H (2015) The research on optimization mathematical model of arrival flights scheduling. Adv Eng Sci 47(6):113–120

    MathSciNet  Google Scholar 

  • Wang S, Li Y, Yang H (2017) Self-adaptive differential evolution algorithm with improved mutation mode. Appl Intell 47:644–658

    Google Scholar 

  • Wang S, Li Y, Yang Y, Liu H (2018) Self-adaptive differential evolution algorithm with improved mutation strategy. Soft Comput 22(10):3433–3447

    Google Scholar 

  • Wang S, Li Y, Yang H (2019) Self-adaptive mutation differential evolution algorithm based on particle swarm optimization. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2019.105496

    Article  Google Scholar 

  • Yu W, Shen M, Chen W et al (2014) Differential evolution with two-level parameter adaptation. IEEE Trans Cybern 44(7):1080–1099

    Google Scholar 

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

    Google Scholar 

  • 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 3816–3821

  • 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):1528–1542

    Google Scholar 

  • 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):30–45

    MathSciNet  MATH  Google Scholar 

  • Zhou Y, Li X, Gao L (2013) A differential evolution algorithm with intersect mutation operator. Appl Soft Comput 13:390–401

    Google Scholar 

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Acknowledgements

The authors sincerely thank the reviewers for their beneficial suggestions.

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Correspondence to Shihao Wang.

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Li, Y., Wang, S. Differential evolution algorithm with elite archive and mutation strategies collaboration. Artif Intell Rev 53, 4005–4050 (2020). https://doi.org/10.1007/s10462-019-09786-5

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