Soft Computing

, Volume 21, Issue 20, pp 5939–5974 | Cite as

New mutation strategies of differential evolution based on clearing niche mechanism

  • Yanan Li
  • Haixiang Guo
  • Xiao Liu
  • Yijing Li
  • Wenwen Pan
  • Bing Gong
  • Shaoning Pang


Although differential evolution (DE) algorithms have been widely proposed for tackling various of problems, the trade-off among population diversity, global and local exploration ability, and convergence rate is hard to maintain with the existing strategies. From this respective, this paper presents some new mutation strategies of DE by applying the clearing niche mechanism to the existing mutation strategies. Insteading of using random, best or target individuals as base vector, the niche individuals are utilized in these strategies. As the base vector is from a subpopulation, which is made up of the best individuals in each niche, the base vector can be guided by the global or local best ones. This mechanism is beneficial to the balance among population diversity, search capability, and convergence rate of DE, since it can both enhance the population diversity and search capability. Extensive experimental results indicate that the proposed strategies based on clearing niche mechanism can effectively enhance DE’s performance.

Graphical Abstract


Differential evolution Niche Clearing mechanism Niche radius 



This research has been supported by National Natural Science Fundation of China under Grant Nos. 71103163, 71573237; New Century Excellent Talents in University of China under Grant No. NCET-13-1012; Research Foundation of Humanities and Social Sciences of Ministry of Education of China No. 15YJA630019; Special Funding for Basic Scientific Research of Chinese Central University under Grant Nos. CUG120111, CUG110411, G2012002A, CUG140604, CUG160605; Open Foundation for the Research Center of Resource Environment Economics in China University of Geosciences (Wuhan); Structure and Oil Resources Key Laboratory Open Project of China under Grant No. TPR-2011-11.

Compliance with ethical standards

Conflict of interest

Author Yanan Li declares that she has no conflict of interest. Author Haixiang Guo declares that he has no conflict of interest. Author Xiao Liu declares that she has no conflict of interest. Author Yijing Li declares that she has no conflict of interest. Author Wenwen Pan declares that she has no conflict of interest. Author Bing Gong declares that he has no conflict of interest. Author Shaoning Pang declares that he has no conflict of interest.

Human and animals participants

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Al-Dabbagh RD, Kinsheel A, Mekhilef S et al (2014) System identification and control of robot manipulator based on fuzzy adaptive differential evolution algorithm. Adv Eng Softw 78:60–66CrossRefGoogle Scholar
  2. Ali M, Ahn CW, Siarry P (2014) Differential evolution algorithm for the selection of optimal scaling factors in image watermarking. Eng Appl Artif Intell 31:15–26CrossRefGoogle Scholar
  3. Basu M (2011) Economic environmental dispatch using multi-objective differential evolution. Appl Soft Comput 11(2):2845–2853CrossRefGoogle Scholar
  4. 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
  5. Brest J, Mernik M (2008) Population size reduction for the differential evolution algorithm. Appl Intell 29(3):228–247CrossRefGoogle Scholar
  6. Brown C, Jin Y, Leach M, et al (2015) \(\mu \)JADE: adaptive differential evolution with a small population. Soft Comput. doi: 10.1007/s00500-015-1746-x
  7. Cai Y, Wang J (2015) Differential evolution with hybrid linkage crossover. Inf Sci 320:244–287MathSciNetCrossRefGoogle Scholar
  8. Das R, Prasad DK (2015) Prediction of porosity and thermal diffusivity in a porous fin using differential evolution algorithm. Swarm Evol Comput 23:27–39CrossRefGoogle Scholar
  9. Epitropakis MG, Li X, Burke EK (2013) A dynamic archive niching differential evolution algorithm for multimodal optimization. In: IEEE Congress on Evolutionary Computation (CEC 2013), pp 79–86Google Scholar
  10. Epitropakis MG, Plagianakos VP, Vrahat MN (2012) Multimodal optimization using niching differential evolution with index-based neighborhoods. In: IEEE Congress on Evolutionary Computation (CEC 2012), pp 1–8Google Scholar
  11. Han MF, Lin CT, Chang JY (2013) Differential evolution with local information for neuro-fuzzy systems optimization. Knowl Based Syst 44:78–89CrossRefGoogle Scholar
  12. Ho-Huu V, Nguyen-Thoi T, Nguyen-Thoi MH et al (2015) An improved constrained differential evolution using discrete variables (D-ICDE) for layout optimization of truss structures. Expert Syst Appl 42(20):7057–7069CrossRefGoogle Scholar
  13. Kundu S, Das S, Vasilakos AV et al (2014) A modified differential evolution-based combined routing and sleep scheduling scheme for lifetime maximization of wireless sensor networks. Soft Comput 19(3):637–659CrossRefGoogle Scholar
  14. Li X (2005) Efficient differential evolution using speciation for multimodal function optimization. In: The 7th annual conference on genetic and evolutionary computation, ACM, pp 873–880Google Scholar
  15. Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Swarm Intelligence Symposium, pp 68–75Google Scholar
  16. Liu JH, Lampinen J (2002) On setting the control parameter of the differential evolution method. In: The 8th international conference on soft computing (MENDEL 2002), pp 11–18Google Scholar
  17. Liu G, Xiong C, Guo Z (2014) Enhanced differential evolution using random-based sampling and neighborhood mutation. Soft Comput 19(8):1–20Google Scholar
  18. Mahfoud SW (1995) Niching methods for genetic algorithms, Ph.D. dissertation, Univ. of Illinois, Urbana-ChampaignGoogle Scholar
  19. Mallipeddi R, Suganthan PN, Pan QK et al (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696CrossRefGoogle Scholar
  20. Mallipeddi R, Lee M (2015) An evolving surrogate model-based differential evolution algorithm. Appl Soft Comput 34:770–787CrossRefGoogle Scholar
  21. Mohamed AW (2015) An improved differential evolution algorithm with triangular mutation for global numerical optimization. Comput Ind Eng 85:359–375CrossRefGoogle Scholar
  22. Mokhtari H, Salmasnia A (2015) A monte carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system. Expert Syst Appl 42(20):7132–7147CrossRefGoogle Scholar
  23. Petrowski A (1996) A clearing procedure as a niching method for genetic algorithms. In: The 1996 IEEE international conference on evolutionary computation, pp 798–803Google Scholar
  24. Petrowski A, Genet MG (1999) A classification tree for speciation. In: IEEE Congress on Evolutionary Computation (CEC 1999), vol 1, pp 204–211Google Scholar
  25. Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: IEEE congress on evolutionary computation (CEC 2005), vol 2, IEEE Press, pp 1785–1791Google Scholar
  26. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417CrossRefGoogle Scholar
  27. Rakshit P, Konar A (2015) Differential evolution for noisy multi-objective optimization. Artif Intell 227:165–189CrossRefzbMATHGoogle Scholar
  28. Sareni B, Krahenbuhl L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evolut Comput 3(2):97–106CrossRefGoogle Scholar
  29. Secmen M, Tasgetiren MF (2013) Ensemble of differential evolution algorithms for electromagnetic target recognition problem. IET Radar Sonar Navig 7(7):780–788CrossRefGoogle Scholar
  30. Sharma S, Rangaiah GP (2013) An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput Chem Eng 56:155–173CrossRefGoogle Scholar
  31. Sheniha SF, Priyadharsini SS, Rajan SE (2013) Removal of artifact from EEG signal using differential evolution algorithm. In: The 2th international conference on communication and signal processing, pp 134–138Google Scholar
  32. Simionescu PA (2014) Computer-aided graphing and simulation tools for AutoCAD User, (1st Ed.) CRC Press, Boca RatonGoogle Scholar
  33. Storn R, Price KV (1995) Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI, USA, Tech. Rep. TR-95-012, [Online]. Available:
  34. Storn R, Price KV (1996) Minimizing the real functions of the ICEC 1996 contest by differential evolution. In: Proceedings: 1996 IEEE international conference on evolutionary computation, pp 842–844Google Scholar
  35. Surjanovic S, Bingham D (2015) Virtual library of simulation experiments: test functions and datasets.
  36. Tang L, Dong Y, Liu J (2015) Differential evolution with an individual-dependent mechanism. IEEE Trans Evolut Comput 19(4):560–574CrossRefGoogle Scholar
  37. Thomsen R (2004) Multimodal optimization using crowding-based differential evolution. In: IEEE Congress on Evolutionary Computation (CEC 2004) vol 2, pp 1382–1389Google Scholar
  38. Tvrdík J, Křivý I (2015) Hybrid differential evolution algorithm for optimal clustering. Appl Soft Comput 35:502–512CrossRefGoogle Scholar
  39. Walters DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8(3):1325–1332CrossRefGoogle Scholar
  40. Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evolut Comput 15(1):55–66CrossRefGoogle Scholar
  41. Wang J, Liao J, Zhou Y et al (2014) Differential evolution enhanced with multiobjective sorting-based mutation operators. IEEE Trans Cybern 44(12):2792–2805CrossRefGoogle Scholar
  42. Yin X, Germay N (1993) A fast genetic algorithm with sharing scheme using cluster analysis methods in multimodal function optimization. In: Artificial Neural Networks and Genetic Algorithms, pp 450–457Google Scholar
  43. Yu WJ, Shen M, Chen WN et al (2014) Differential evolution with two-level parameter adaptation. IEEE Trans Cybern 44(7):1080–1099CrossRefGoogle Scholar
  44. Zaharie D (2009) Influence of crossover on the behavior of differential evolution algorithm. Appl Soft Comput 9(3):1126–1138CrossRefGoogle Scholar
  45. Zhai Z, Li X (2011) A dynamic archive based niching particle swarm optimizer using a small population size. In: Proceedings of the Australian Computer Science Conference (ACSC 2011), ACM, pp 1–7Google Scholar
  46. Zhang H, Yue D, Xie X et al (2015) Multi-elite guide hybrid differential evolution with simulated annealing technique for dynamic economic emission dispatch. Appl Soft Comput 34:312–323CrossRefGoogle Scholar
  47. Zhang G, Li D, Zhou X, et al (2015) Differential evolution with dynamic niche radius strategy for multimodal optimization. In: The 27th international conference on control and decision conference (CCDC 2015), IEEE, pp 3059–3064Google Scholar
  48. Zhang JQ, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13(5):945–958CrossRefGoogle Scholar
  49. Zhu W, Fang JA, Tang Y et al (2012) Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size. PloS One 7(7):e40549CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.College of Economics and ManagementChina University of GeosciencesWuhanChina
  2. 2.Research Center for Digital Business ManagementChina University of GeosciencesWuhanChina
  3. 3.Mineral Resource Strategy and Policy Research Center of China University of GeosciencesWuhanChina
  4. 4.The Joseph M. Katz Graduate School of BusinessUniversity of PittsburghPittsburghUSA
  5. 5.Department of Industrial Engineering, Business Administration and Statistic E.T.S Industrial EngineeringUniversidad Politécnica de Madrid C/José Gutiérrez Abascal 2MadridSpain
  6. 6.Department of ComputingUnitec Institute of TechnologyAucklandNew Zealand

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