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Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems

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Abstract

This paper describes a dynamic group-based differential evolution (GDE) algorithm for global optimization problems. The GDE algorithm provides a generalized evolution process based on two mutation operations to enhance search capability. Initially, all individuals in the population are grouped into a superior group and an inferior group based on their fitness values. The two groups perform different mutation operations. The local mutation model is applied to individuals with better fitness values, i.e., in the superior group, to search for better solutions near the current best position. The global mutation model is applied to the inferior group, which is composed of individuals with lower fitness values, to search for potential solutions. Subsequently, the GDE algorithm employs crossover and selection operations to produce offspring for the next generation. In this paper, an adaptive tuning strategy based on the well-known 1/5th rule is used to dynamically reassign the group size. It is thus helpful to trade off between the exploration ability and the exploitation ability. To validate the performance of the GDE algorithm, 13 numerical benchmark functions are tested. The simulation results indicate that the approach is effective and efficient.

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References

  1. Carlos CCA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287

    Article  MATH  Google Scholar 

  2. Fei P, Ke T, Guoliang C, Xin Y (2010) Population-based algorithm portfolios for numerical optimization. IEEE Trans Evol Comput 14:782–800

    Article  Google Scholar 

  3. Fogel DB (1995) Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press, New York

    Google Scholar 

  4. Salto C, Alba E (2012) Designing heterogeneous distributed GAs by efficiently self-adapting the migration period. Appl Intell 36(4):800–808

    Article  Google Scholar 

  5. Gacto MJ, Alcalá R, Herrera F (2012) A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems. Appl Intell 36(2):330–347

    Article  Google Scholar 

  6. Shin KS, Jeong Y-S, Jeong MK (2012) A two-leveled symbiotic evolutionary algorithm for clustering problems. Appl Intell 36(4):788–799

    Article  Google Scholar 

  7. Ayvaz D, Topcuoglu HR, Gurgen F (2012) Performance evaluation of evolutionary heuristics in dynamic environments. Appl Intell 37(1):130–144

    Article  Google Scholar 

  8. Korkmaz EE (2010) Multi-objective genetic algorithms for grouping problems. Appl Intell 33(2):179–192

    Article  Google Scholar 

  9. Chu C-P, Chang Y-C, Tsai C-C (2011) PC2PSO: personalized e-course composition based on particle swarm optimization. Appl Intell 34(1):141–154

    Article  Google Scholar 

  10. Ali YMB (2012) Psychological model of particle swarm optimization based multiple emotions. Appl Intell 36(3):649–663

    Article  Google Scholar 

  11. Wang K, Zheng YJ (2012) A new particle swarm optimization algorithm for fuzzy optimization of armored vehicle scheme design. Appl Intell. doi:10.1007/s10489-012-0345-0

  12. Xing H, Qu R (2012) A compact genetic algorithm for the network coding based resource minimization problem. Appl Intell 36(4):809–823

    Article  Google Scholar 

  13. Özyer T, Zhang M, Alhajj R (2011) Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation. Appl Intell 35(1):110–122

    Article  Google Scholar 

  14. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102

    Article  Google Scholar 

  15. Bäck TT, Schwefel H-P (2002) Evolution strategies: a comprehensive introduction. Natural Computing, 3–52

  16. Kennedy J, Eberhart R (1995) Particle swarm optimization. Paper presented at the IEEE int neural netw

  17. Norouzzadeh MS, Ahmadzadeh MR, Palhang M (2012) LADPSO: using fuzzy logic to conduct PSO algorithm. Appl Intell 37(2):290–304

    Article  Google Scholar 

  18. Ali YMB (2012) Psychological model of particle swarm optimization based multiple emotions. Appl Intell 36(3):649–663

    Article  Google Scholar 

  19. Shuang B, Chen J, Li Z (2011) Study on hybrid PS-ACO algorithm. Appl Intell 34(1):64–73

    Article  Google Scholar 

  20. Masoud H, Jalili S, Hasheminejad SMH (2012) Dynamic clustering using combinatorial particle swarm optimization. Appl Intell. doi:10.1007/s10489-012-0373-9

  21. Price K, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin

    Google Scholar 

  22. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  MathSciNet  MATH  Google Scholar 

  23. Salto C, Alba E (2012) Designing heterogeneous distributed GAs by efficiently self-adapting the migration period. Appl Intell 36(4):800–808

    Article  Google Scholar 

  24. Araújo AFR, Garrozi C (2010) MulRoGA: a multicast routing genetic algorithm approach considering multiple objectives. Appl Intell 32(3):330–345

    Article  Google Scholar 

  25. Lin C-T, Han M-F, Lin Y-Y, Liao S-H, Chang J-Y (2011) Neuro-fuzzy system design using differential evolution with local information. In: 2011 IEEE international conference on fuzzy systems (FUZZ), pp 1003–1006

    Chapter  Google Scholar 

  26. Junhong L, Jouni L (2002) A fuzzy adaptive differential evolution algorithm. In: TENCON ’02. Proceedings. 2002 IEEE region 10 conference on computers, communications, control and power engineering, vol 1, pp 606–611

    Chapter  Google Scholar 

  27. Xue F, Sanderson AC, Bonissone PP, Graves RJ (2005) Fuzzy logic controlled multiobjective differential evolution. Paper presented at the IEEE int conf fuzzy syst

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

    Article  Google Scholar 

  29. Cai Z, Gong W, Ling CX, Zhang H (2011) A clustering-based differential evolution for global optimization. Appl Soft Comput 11:1363–1379

    Article  Google Scholar 

  30. Chen C-H, Lin C-J, Lin C-T (2009) Nonlinear system control using adaptive neural fuzzy networks based on a modified differential evolution. IEEE Trans Syst Man Cybern, Part C, Appl Rev 39:459–473

    Article  Google Scholar 

  31. Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13:526–553

    Article  Google Scholar 

  32. Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15:4–31

    Article  Google Scholar 

  33. Cheshmehgaz HR, Desa MI, Wibowo A (2012) Effective local evolutionary searches distributed on an island model solving bi-objective optimization problems. Appl Intell. doi:10.1007/s10489-012-0375-7

  34. Vafashoar R, Meybodi MR, Momeni Azandaryani AH (2012) CLA-DE: a hybrid model based on cellular learning automata for numerical optimization. Appl Intell 36(3):735–748

    Article  Google Scholar 

  35. Jingqiao Z, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13:945–958

    Article  Google Scholar 

  36. Mezura-Montes E, Miranda-Varela ME, del Carmen Gmez-Ramn R (2010) Differential evolution in constrained numerical optimization: an empirical study. Inf Sci 180:4223–4262

    Article  MATH  Google Scholar 

  37. Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12:107–125

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: The 2005 IEEE congress on evolutionary computation, 2005, vol 2, pp 1785–1791

    Chapter  Google Scholar 

  40. Rahnamayan S, Tizhoosh HR, Salama MMA (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12:64–79

    Article  Google Scholar 

  41. Su M-T, Chen C-H, Lin C-J, Lin C-T (2011) A rule-based symbiotic modified differential evolution for self-organizing neuro-fuzzy systems. Appl Soft Comput 11:4847–4858

    Article  Google Scholar 

  42. Wenyin G, Zhihua C, Ling CX, Hui L (2011) Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans Syst Man Cybern, Part B, Cybern 41:397–413

    Article  Google Scholar 

  43. Vesterstrom J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on evolutionary computation, 2004 (CEC2004), vol 2, pp 1980–1987

    Google Scholar 

  44. Lin CT, Han MF, Lin YY, Chang JY, Ko LW (2010) Differential evolution based optimization of locally recurrent neuro-fuzzy system for dynamic system identification. Paper presented at the 17th national conference on fuzzy theory and its applications

  45. Josef T (2009) Adaptation in differential evolution: a numerical comparison. Appl Soft Comput 9:1149–1155

    Article  Google Scholar 

  46. Bäck TT, Schwefel H-P (1995) Evolution strategies I: variants and their computational implementation. In: Genetic algorithms in engineering and computer science, pp 111–126

    Google Scholar 

  47. Beyer HG, Schwefel HP (2002) Evolution strategies: a comprehensive introduction. Nat Comput 3–52

  48. Shang Y-W, Qiu Y-H (2006) A note on the extended Rosenbrock function. Evol Comput 14:119–126

    Article  Google Scholar 

  49. Yao X, Liu Y, Liang K-H, Lin G (2003) Fast evolutionary algorithms. Paper presented at the advances evol computing: theory applicat, New York

  50. 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 10:646–657

    Article  Google Scholar 

  51. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  52. García S, Herrera F (2008) An extension on “Statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J Mach Learn Res 9:2677–2694

    MATH  Google Scholar 

  53. Alam MS, Islam MM, Xin Yao F, Murase K (2011) Recurring two-stage evolutionary programming: a novel approach for numeric optimization. IEEE Trans Syst Man Cybern, Part B, Cybern 41:1352–1365

    Article  Google Scholar 

  54. Lee C, Yao X (2004) Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Trans Evol Comput 8:1–13

    Article  Google Scholar 

  55. Yang Z, He J, Yao X (2007) Making a difference to differential evolution. In: Advances metaheuristics hard optimization, pp 397–414

    Google Scholar 

Download references

Acknowledgements

This work was supported by Department of Industrial Technology under grants 100-EC-17-A-02-S1-032, by the UST-UCSD International Center of Excellence in Advanced Bioengineering sponsored by the Taiwan National Science Council I-RiCE Program under Grant Number NSC-100-2911-I-009-101 and by the Aiming for the Top University Plan of National Chiao Tung University, the Ministry of Education, Taiwan, under Contract 100W9633 & 101W963.

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  1. Institute of Electrical Control Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan, ROC

    Ming-Feng Han, Shih-Hui Liao, Jyh-Yeong Chang & Chin-Teng Lin

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  1. Ming-Feng Han
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  2. Shih-Hui Liao
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  3. Jyh-Yeong Chang
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Correspondence to Ming-Feng Han.

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Han, MF., Liao, SH., Chang, JY. et al. Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems. Appl Intell 39, 41–56 (2013). https://doi.org/10.1007/s10489-012-0393-5

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