Dual-Subpopulation as reciprocal optional external archives for differential evolution


Autoři: Haiming Du aff001;  Zaichao Wang aff001;  Yiqun Fan aff002;  Chengjun Li aff002;  Juan Yao aff003
Působiště autorů: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China aff001;  School of Computer Science, China University of Geosciences, Wuhan, Hubei, China aff002;  College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222103

Souhrn

Differential Evolution (DE) is powerful for global optimization problems. Among DE algorithms, JADE and its variants, whose mutation strategy is DE/current-to-pbest/1 with optional archive, have good performance. A significant feature of the above mutation strategy is that one individual for difference operation comes from the union of the optional external archive and the population. In existing DE algorithms based on the mutation strategy—JADE and its variants, individuals eliminated from the population are send to the archive. In this paper, we propose a scheme for managing the optional external archive. According to our scheme, two subpopulations are maintained in the population. Each of them regards the other as the archive. In experiments, our scheme is applied in JADE and two of its variants—SHADE and L-SHADE. Experimental results show that our scheme can enhance JADE and its variants. Moreover, it can be seen that L-SHADE with our scheme performs significantly better than four DE algorithms, CoBiDE, MPEDE, EDEV, and MLCCDE.

Klíčová slova:

Research and analysis methods – Research facilities – Information centers – Archives – Simulation and modeling – Evolutionary algorithms – Computational techniques – Evolutionary computation – Physical sciences – Mathematics – Applied mathematics – Algorithms – Optimization – Biology and life sciences – Population biology – Population metrics – Population size – Evolutionary biology – Evolutionary processes – Natural selection – Convergent evolution – Ecology – Ecological metrics – Species diversity – Ecology and environmental sciences


Zdroje

1. Yang M, Li C, Cai Z, Guan J. Differential evolution with auto-enhanced population diversity. IEEE transactions on cybernetics. 2015;45(2):302–315. doi: 10.1109/TCYB.2014.2339495 25095277

2. Das S, Mullick SS, Suganthan PN. Recent advances in differential evolution—An updated survey. Swarm and Evolutionary Computation. 2016;27:1–30. doi: 10.1016/j.swevo.2016.01.004

3. Zhang J, Sanderson AC. JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation. 2009;13(5):945–958. doi: 10.1109/TEVC.2009.2014613

4. Wang Y, Cai Z, Zhang Q. Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation. 2011;15(1):55–66. doi: 10.1109/TEVC.2010.2087271

5. Yu WJ, Shen M, Chen WN, Zhan ZH, Gong YJ, Lin Y, et al. Differential Evolution With Two-Level Parameter Adaptation. IEEE Transactions on Cybernetics. 2014;44(7):1080–1099. doi: 10.1109/TCYB.2013.2279211 24013834

6. Wang Y, Li HX, Huang T, Li L. Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Applied Soft Computing. 2014;18:232–247. doi: 10.1016/j.asoc.2014.01.038

7. Li YL, Zhan ZH, Gong YJ, Chen WN, Zhang J, Li Y. Differential evolution with an evolution path: A DEEP evolutionary algorithm. IEEE transactions on cybernetics. 2015;45(9):1798–1810. doi: 10.1109/TCYB.2014.2360752 25314717

8. Tang L, Dong Y, Liu J. Differential evolution with an individual-dependent mechanism. IEEE Transactions on Evolutionary Computation. 2015;19(4):560–574. doi: 10.1109/TEVC.2014.2360890

9. Awad NH, Ali MZ, Suganthan PN, Reynolds RG. An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In: Evolutionary Computation (CEC), 2016 IEEE Congress on. IEEE; 2016. p. 2958–2965.

10. Fan Q, Yan X. Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. IEEE transactions on cybernetics. 2016;46(1):219–232. doi: 10.1109/TCYB.2015.2399478 25775502

11. Li G, Lin Q, Cui L, Du Z, Liang Z, Chen J, et al. A novel hybrid differential evolution algorithm with modified CoDE and JADE. Applied Soft Computing. 2016;47:577–599. doi: 10.1016/j.asoc.2016.06.011

12. Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H. Differential evolution with multi-population based ensemble of mutation strategies. Information Sciences. 2016;329:329–345. doi: 10.1016/j.ins.2015.09.009

13. Fu C, Jiang C, Chen G, Liu Q. An adaptive differential evolution algorithm with an aging leader and challengers mechanism. Applied Soft Computing. 2017;57:60–73. doi: 10.1016/j.asoc.2017.03.032

14. Guo Z, Liu G, Li D, Wang S. Self-adaptive differential evolution with global neighborhood search. Soft Computing. 2017;21(13):3759–3768. doi: 10.1007/s00500-016-2029-x

15. Mohamed AW, Suganthan PN. Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Computing. 2017; p. 1–21.

16. Ali MZ, Awad NH, Suganthan PN, Reynolds RG. An adaptive multipopulation differential evolution with dynamic population reduction. IEEE Transactions on Cybernetics. 2017;47(9):2768–2779. doi: 10.1109/TCYB.2016.2617301 28113798

17. Ghosh A, Das S, Mullick SS, Mallipeddi R, Das AK. A switched parameter differential evolution with optional blending crossover for scalable numerical optimization. Applied Soft Computing. 2017;57:329–352. doi: 10.1016/j.asoc.2017.03.003

18. Tatsis VA, Parsopoulos KE. Differential evolution with grid-based parameter adaptation. Soft Computing. 2017;21(8):2105–2127. doi: 10.1007/s00500-015-1911-2

19. Tian M, Gao X, Dai C. Differential evolution with improved individual-based parameter setting and selection strategy. Applied Soft Computing. 2017;56:286–297. doi: 10.1016/j.asoc.2017.03.010

20. Zhou YZ, Yi WC, Gao L, Li XY. Adaptive differential evolution with sorting crossover rate for continuous optimization problems. IEEE Transactions on Cybernetics. 2017;47(9):2742–2753. doi: 10.1109/TCYB.2017.2676882 28362602

21. Wu G, Shen X, Li H, Chen H, Lin A, Suganthan P. Ensemble of differential evolution variants. Information Sciences. 2018;423:172–186. doi: 10.1016/j.ins.2017.09.053

22. Rakshit P, Konar A, Bhowmik P, Goswami I, Das S, Jain LC, et al. Realization of an Adaptive Memetic Algorithm Using Differential Evolution and Q-Learning: A Case Study in Multirobot Path Planning. IEEE Transactions on Systems Man and Cybernetics Part B. 2013;43(4):814–831. doi: 10.1109/TSMCA.2012.2226024

23. Das S, Mandal A, Mukherjee R. An adaptive differential evolution algorithm for global optimization in dynamic environments. IEEE Transactions on Cybernetics. 2014;44(6):966. doi: 10.1109/TCYB.2013.2278188 23996590

24. Ali MZ, Awad NH, Suganthan PN. Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization. Applied Soft Computing. 2015;33:304–327. doi: 10.1016/j.asoc.2015.04.019

25. Guo SM, Yang CC. Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Transactions on Evolutionary Computation. 2015;19(1):31–49. doi: 10.1109/TEVC.2013.2297160

26. Guo SM, Yang CC, Hsu PH, Tsai JSH. Improving differential evolution with a successful-parent-selecting framework. IEEE Transactions on Evolutionary Computation. 2015;19(5):717–730. doi: 10.1109/TEVC.2014.2375933

27. Xu Y, Fang Ja, Zhu W, Wang X, Zhao L. Differential evolution using a superior–inferior crossover scheme. Computational Optimization and Applications. 2015;61(1):243–274. doi: 10.1007/s10589-014-9701-9

28. Cui L, Li G, Lin Q, Chen J, Lu N. Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Computers & Operations Research. 2016;67:155–173. doi: 10.1016/j.cor.2015.09.006

29. Ghasemi M, Taghizadeh M, Ghavidel S, Abbasian A. Colonial competitive differential evolution: An experimental study for optimal economic load dispatch. Applied Soft Computing. 2016;40:342–363. doi: 10.1016/j.asoc.2015.11.033

30. Liao J, Cai Y, Wang T, Tian H, Chen Y. Cellular direction information based differential evolution for numerical optimization: an empirical study. Soft Computing. 2016;20(7):2801–2827. doi: 10.1007/s00500-015-1682-9

31. Qiu X, Tan KC, Xu JX. Multiple exponential recombination for differential evolution. IEEE transactions on cybernetics. 2017;47(4):995–1006. doi: 10.1109/TCYB.2016.2536167

32. Yi W, Zhou Y, Gao L, Li X, Mou J. An improved adaptive differential evolution algorithm for continuous optimization. Expert Systems with Applications. 2016;44:1–12. doi: 10.1016/j.eswa.2015.09.031

33. Awad NH, Ali MZ, Suganthan PN, Reynolds RG. CADE: A hybridization of Cultural Algorithm and Differential Evolution for numerical optimization. Information Sciences. 2017;378:215–241. doi: 10.1016/j.ins.2016.10.039

34. Du W, Leung SYS, Tang Y, Vasilakos AV. Differential evolution with event-triggered impulsive control. IEEE transactions on cybernetics. 2017;47(1):244–257. doi: 10.1109/TCYB.2015.2512942 26800559

35. Zheng LM, Liu L, Zhang SX, Zheng SY. Enhancing differential evolution with interactive information. Soft Computing. 2017; p. 1–20.

36. Ghosh A, Das S, Mallipeddi R, Das AK, Dash SS. A Modified Differential Evolution With Distance-based Selection for Continuous Optimization in Presence of Noise. IEEE Access. 2017;5:26944–26964. doi: 10.1109/ACCESS.2017.2773825

37. Zhang X, Zhang X. Improving differential evolution by differential vector archive and hybrid repair method for global optimization. Soft Computing. 2017;21(23):7107–7116. doi: 10.1007/s00500-016-2253-4

38. Zheng LM, Zhang SX, Tang KS, Zheng SY. Differential evolution powered by collective information. Information Sciences. 2017;399:13–29. doi: 10.1016/j.ins.2017.02.055

39. Zhou XG, Zhang GJ. Abstract Convex Underestimation Assisted Multistage Differential Evolution. IEEE Transactions on Cybernetics. 2017;PP(99):1–12.

40. Cui L, Li G, Zhu Z, Lin Q, Wong KC, Chen J, et al. Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism. Information Sciences. 2018;422:122–143. doi: 10.1016/j.ins.2017.09.002

41. Brest J, Maučec MS. Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft Computing. 2011;15(11):2157–2174. doi: 10.1007/s00500-010-0644-5

42. Yang M, Cai Z, Li C, Guan J. An improved adaptive differential evolution algorithm with population adaptation. In: Conference on Genetic and Evolutionary Computation; 2013. p. 145–152.

43. Zhu W, Tang Y, Fang JA, Zhang W. Adaptive population tuning scheme for differential evolution. Information Sciences. 2013;223(2):164–191. doi: 10.1016/j.ins.2012.09.019

44. Mallipeddi R, Wu G, Lee M, Suganthan PN. Gaussian adaptation based parameter adaptation for differential evolution. In: Evolutionary Computation; 2014. p. 1760–1767.

45. Tanabe R, Fukunaga AS. Improving the search performance of SHADE using linear population size reduction. In: Evolutionary Computation (CEC), 2014 IEEE Congress on. IEEE; 2014. p. 1658–1665.

46. Gonuguntla V, Mallipeddi R, Veluvolu KC. Differential Evolution with Population and Strategy Parameter Adaptation. Mathematical Problems in Engineering. 2015;2015(287607):10–19.

47. Tanabe R, Fukunaga A. Success-history based parameter adaptation for differential evolution. In: Evolutionary Computation (CEC), 2013 IEEE Congress on. IEEE; 2013. p. 71–78.

48. Gong W, Cai Z, Wang Y. Repairing the crossover rate in adaptive differential evolution. Applied Soft Computing. 2014;15:149–168. doi: 10.1016/j.asoc.2013.11.005

49. Alba E, Tomassini M. Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation. 2002;6(5):443–462. doi: 10.1109/TEVC.2002.800880

50. Zhang SX, Zheng LM, Tang KS, Zheng SY, Chan WS. Multi-layer competitive-cooperative framework for performance enhancement of differential evolution. Information Sciences. 2019;482:86–104. doi: 10.1016/j.ins.2018.12.065

51. Brest J, Greiner S, Boskovic B, Mernik M, Zumer V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation. 2006;10(6):646–657. doi: 10.1109/TEVC.2006.872133

52. Qin AK, Huang VL, Suganthan PN. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE transactions on Evolutionary Computation. 2009;13(2):398–417. doi: 10.1109/TEVC.2008.927706

53. Trelea IC. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters. 2003;85(6):317–325. doi: 10.1016/S0020-0190(02)00447-7

54. Brest J, Maučec MS. Population size reduction for the differential evolution algorithm. Applied Intelligence. 2008;29(3):228–247. doi: 10.1007/s10489-007-0091-x

55. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing. 2011;11(2):1679–1696. doi: 10.1016/j.asoc.2010.04.024

56. Mallipeddi R, Suganthan PN. Differential evolution algorithm with ensemble of parameters and mutation and crossover strategies. In: International Conference on Swarm, Evolutionary, and Memetic Computing. Springer; 2010. p. 71–78.

57. Storn R, Price K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization. 1997;11(4):341–359. doi: 10.1023/A:1008202821328

58. Epitropakis MG, Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN. Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Transactions on Evolutionary Computation. 2011;15(1):99–119. doi: 10.1109/TEVC.2010.2083670

59. Dorronsoro B, Bouvry P. Improving classical and decentralized differential evolution with new mutation operator and population topologies. IEEE Transactions on Evolutionary Computation. 2011;15(1):67–98. doi: 10.1109/TEVC.2010.2081369

60. Liang JJ, Qin AK, Suganthan PN, Baskar S. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on Evolutionary Computation. 2006;10(3):281–295. doi: 10.1109/TEVC.2005.857610

61. Auger A, Hansen N. A restart CMA evolution strategy with increasing population size. In: Evolutionary Computation, 2005. The 2005 IEEE Congress on. vol. 2. IEEE; 2005. p. 1769–1776.

62. Gao W, Yen GG, Liu S. A dual-population differential evolution with coevolution for constrained optimization. IEEE Transactions on Cybernetics. 2015;45(5):1094–1107. doi: 10.1109/TCYB.2014.2345478 25137739


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