DSM-DE: a differential evolution with dynamic speciation-based mutation for single-objective optimization

  • Libao DengEmail author
  • Lili Zhang
  • Haili Sun
  • Liyan Qiao
Regular Research Paper


A new differential evolution algorithm with two dynamic speciation-based mutation strategies (DSM-DE) is proposed to solve single-objective optimization problems. An explorative mutation “DE/seeds-to-seeds” and an exploitative mutation “DE/seeds-to-rand” are employed simultaneously in DSM-DE in the evolutionary process. A Dynamic Speciation Technique is designed to assist the two mutations in order to utilize the potential of selective portioning of critical individuals in the population. It dynamically divides the population into numbers of species whilst taking species seeds as centers. The best individuals for each species are used as base vectors in each species in the proposed mutation strategies. “DE/seeds-to-seeds” selects individuals from species seeds and current species to constitute difference vectors whereas “DE/seeds-to-rand” selects from the whole population. Thus the two mutation strategies can accelerate the convergence process without decreasing diversity of the population. Comparison results with four classic DE variants, one state-of-art DE variant and two improved non-DE variants on CEC2014, CEC2015 benchmark, and Lennard-Jones potential problem reveal that the overall performance of DSM-DE is better than that of the other seven DE algorithms. In addition, experiments also substantiate the effectiveness and superiority of two seeds-guided mutation strategies in DSM-DE.


Differential evolution Mutation strategy Dynamic speciation Single-objective optimization 



  1. 1.
    Al-Dabbagh RD, Neri F, Idris N, Baba MS (2018) Algorithmic design issues in adaptive differential evolution schemes: review and taxonomy. Swarm Evol Comput 43:284–311CrossRefGoogle Scholar
  2. 2.
    Awadallah MA, Al-Betar MA, Bolaji AL, Alsukhni EM, Al-Zoubi H (2018) Natural selection methods for artificial bee colony with new versions of onlooker bee. Soft Comput.
  3. 3.
    Biswas S, Kundu S, Das S (2014) An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution. IEEE Trans Cybern 44(10):1726–1737CrossRefGoogle Scholar
  4. 4.
    Biswas S, Kundu S, Das S (2015) Inducing niching behavior in differential evolution through local information sharing. IEEE Trans Evol Comput 19(2):246–263CrossRefGoogle Scholar
  5. 5.
    Brest J, Greiner S, Bokovic 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. 6.
    Cai Y, Wang J (2013) Differential evolution with neighborhood and direction information for numerical optimization. IEEE Trans Cybern 43(6):2202–2215CrossRefGoogle Scholar
  7. 7.
    Cui L, Li G, Lin Q, Chen J, Lu N (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155–173MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution-an updated survey. Swarm Evol Comput 27:1–30CrossRefGoogle Scholar
  9. 9.
    Fan Q, Yan X (2016) Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. IEEE Trans Cybern 46(1):219–232CrossRefGoogle Scholar
  10. 10.
    Gao WF, Yen GG, Liu SY (2015) A dual-population differential evolution with coevolution for constrained optimization. IEEE Trans Cybern 45(5):1094–1107Google Scholar
  11. 11.
    Gong W, Cai Z (2013) Differential evolution with ranking-based mutation operators. IEEE Trans Cybern 43(6):2066–2081CrossRefGoogle Scholar
  12. 12.
    Gupta S, Deep K (2018) A novel random walk grey wolf optimizer. Swarm Evol Comput 44:101–112CrossRefGoogle Scholar
  13. 13.
    He X, Zhou Y (2018) Enhancing the performance of differential evolution with covariance matrix self-adaptation. Appl Soft Comput J 64:227–243CrossRefGoogle Scholar
  14. 14.
    Hui S, Suganthan PN (2016) Ensemble and arithmetic recombination-based speciation differential evolution for multimodal optimization. IEEE Trans Cybern 46(1):64–74CrossRefGoogle Scholar
  15. 15.
    Liang JJ, Qu BY, Suganthan PN (2014) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical reportGoogle Scholar
  16. 16.
    Liang JJ, Qu BY, PNSQC (2015) Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical reportGoogle Scholar
  17. 17.
    Lee CY, Yao X (2004) Evolutionary programming using mutations based on the Levy probability distribution. IEEE Trans Evol Comput 8(1):1–13CrossRefGoogle Scholar
  18. 18.
    Li X (2005) Efficient differential evolution using speciation for multimodal function optimization. In: Proceedings of the conference on genetic and evolutionary computationGoogle Scholar
  19. 19.
    Li Y, Guo H, Liu X, Li Y, Pan W, Gong B, Pang S (2017) New mutation strategies of differential evolution based on clearing niche mechanism. Soft Comput 21(20):5939–5974CrossRefGoogle Scholar
  20. 20.
    Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45, Article 35Google Scholar
  21. 21.
    Mohamed AW, Suganthan PN (2018) Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput 22(10):1–21CrossRefGoogle Scholar
  22. 22.
    Neri F, Tirronen V (2009) Scale factor local search in differential evolution. Memet Comput 1(2):153–171CrossRefGoogle Scholar
  23. 23.
    Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33(1–2):61–106CrossRefGoogle Scholar
  24. 24.
    Poikolainen I, Neri F, Caraffini F (2015) Cluster-based population initialization for differential evolution frameworks. Inf Sci 297:216–235CrossRefGoogle Scholar
  25. 25.
    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
  26. 26.
    Sharifi-Noghabi H, Rajabi Mashhadi H, Shojaee K (2017) A novel mutation operator based on the union of fitness and design spaces information for differential evolution. Soft Comput 21(22):6555–6562CrossRefGoogle Scholar
  27. 27.
    Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Sun G, Cai Y, Wang T, Tian H, Wang C, Chen Y (2018) Differential evolution with individual-dependent topology adaptation. Inf Sci 450:1–38MathSciNetCrossRefGoogle Scholar
  29. 29.
    Swagatam D, Suganthan PN (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Technical reportGoogle Scholar
  30. 30.
    Wang J, Zhang W, Zhang J (2015) Cooperative differential evolution with multiple populations for multiobjective optimization. IEEE Trans Cybern 46(12):1–14Google Scholar
  31. 31.
    Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66CrossRefGoogle Scholar
  32. 32.
    Zhang J, Sanderson AC (2009) Jade: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958CrossRefGoogle Scholar
  33. 33.
    Zheng LM, Zhang SX, Zheng SY, Pan YM (2016) Differential evolution algorithm with two-step subpopulation strategy and its application in microwave circuit designs. IEEE Trans Ind Inform 12(3):911–923CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and TechnologyHarbin Institute of TechnologyWeihaiChina

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