Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

An elitism-based self-adaptive multi-population Jaya algorithm and its applications

  • 324 Accesses

  • 7 Citations

Abstract

This study proposes an elitist-based self-adaptive multi-population (SAMPE) Jaya algorithm to solve the constrained and unconstrained problems related to numerical and engineering optimization. The Jaya algorithm is a newly developed metaheuristic-based optimization algorithm, and it does not require any algorithmic-specific parameters to be set other than the common control parameters of number of iterations and population size. The search mechanism of the Jaya algorithm is improved in this work by using the subpopulation search scheme with elitism. It uses an adaptive scheme for dividing the population into subpopulations. The effectiveness of the proposed SAMPE-Jaya algorithm is verified on CEC 2015 benchmark problems in addition to fifteen unconstrained, six constrained standard benchmark problems and four constrained mechanical design optimization problems considered from the literature. The Friedman rank test is also done for comparing the performance of the SAMPE-Jaya algorithm with other algorithms. It is also tested on three large-scale problems with the dimensions of 100, 500 and 1000. Furthermore, the proposed SAMPE-Jaya algorithm is used for solving a case study of design optimization of a micro-channel heat sink. The computational experiments have proved the effectiveness of the proposed SAMPE-Jaya algorithm.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Amirjanov A (2006) The development of a changing range genetic algorithm. Comput Methods Appl Mech Eng 195:2495–2508

  2. Andersson M, Bandaru S, Ng AHC, Syberfeldt A (2015) Parameter tuned CMA-ES on the CEC’15 expensive problems. In: IEEE congress on evolutionary computation, Sendai, Japan, 2015

  3. Becerra R, Coello CAC (2006) Cultured differential evolution for constrained optimization. Comput Methods Appl Mech Eng 195:4303–4322

  4. Bergh FV, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evolut Comput 8(3):225–239

  5. Branke J, Kaußler T, Schmidt C, Schmeck H (2000) A multi-population approach to dynamic optimization problems. Adaptive computing in design and manufacturing. Springer, Berlin, pp 299–308

  6. Cantu-Paz E (1998) A survey of parallel genetic algorithms. IllGAL report 97003, The University of Illinois, 1997. ftp://ftp-lligal.ge.uiuc.edu/pub/papers/IlliGALs/97003.ps.Z

  7. Cheng R, Jin Y (2015) A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern 45(2):191–204

  8. Coello CAC, Becerra RL (2004) Efficient evolutionary optimization through the use of a cultural algorithm. Eng Optim 36:219–236

  9. Cruz C, González JR, Pelta DA (2011) Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput 15(7):1427–1448

  10. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Sixth international symposium on micro machine and human science, Nagoya, Japan, pp 39–43

  11. Hamida SB, Schoenauer M (2002) ASCHEA: new results using adaptive segregational constraint handling. In: Proceedings of the world on congress on computational intelligence, pp 884–889

  12. Haupt RL, Haupt SE (2004) Practical genetic algorithms, 2nd edn. Wiley, Hoboken

  13. Husain V, Kim KY (2010) Enhanced multi-objective optimization of a micro-channel heat sink through evolutionary algorithm coupled with multiple surrogate models. Appl Therm Eng 30:1683–1691

  14. Irawan CA, Salhi S, Drezner ZJ (2016) Heuristics: hybrid meta-heuristics with VNS and exact methods: application to large unconditional and conditional vertex p-centre problems. J Heuristics 22(4):507–537

  15. Jin N, Rahmat-Samii Y (2010) Hybrid real-binary particle swarm optimization (HPSO) in engineering electromagnetic. IEEE Trans Antennas Propag 58(12):3786–3794

  16. Joaquin D, Salvador G, Daniel M, Francisco H (2016) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18

  17. Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: LNAI 4529. Springer, Berlin, pp 789–798

  18. Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69–84

  19. Kohli M, Arora S (2017) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Des Eng. https://doi.org/10.1016/j.jcde.2017.02.005

  20. Koziel S, Michalewicz Z (1999) Evolutionary algorithms, homomorphous mappings and constrained parameter optimization. IEEE Trans Evolut Comput 7:19–44

  21. Lampinen J (2002) A constraint handling approach for the differential evolution algorithm. In: IEEE congress on evolutionary computation, vol 2, pp 1468–1473

  22. Lau HC, Raidl GR, Van Hentenryck PJ (2016) New developments in metaheuristics and their applications. J Heuristics 22:359

  23. Li C, Yang S (2008) Fast multi-swarm optimization for dynamic optimization problems. In: Fourth international conference on natural computation, ICNC’08, vol 7. IEEE, pp 624–628

  24. Li C, Nguyen TT, Yang M, Yang S, Zeng S (2015) Multi-population methods in un-constrained continuous dynamic environments: the challenges. Inf Sci 296:95–118

  25. Liang JJ, Qin AK (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–295

  26. Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640

  27. Mambrini A, Sudholt D (2014) Design and analysis of adaptive migration intervals in parallel evolutionary algorithms. In: Proceedings of the 2014 annual conference on genetic and evolutionary computation, pp 1047–1054

  28. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, may be better. IEEE Trans Evolut Comput 8(3):204–210

  29. Mezura-Montes E, Coello CAC (2006) A simple multi membered evolution strategy to solve constrained optimization problems. IEEE Trans Evolut Comput 9:1–17

  30. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

  31. Ngo TT, Sadollahb AJ, Kim H (2016) A cooperative particle swarm optimizer with stochastic movements for computationally expensive numerical optimization problems. J Comput Sci 13:68–82

  32. Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evolut Comput 6:1–24

  33. Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658–3670

  34. Nseef SK, Abdullah S, Turky A, Kendall G (2016) An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. Knowl Based Syst 104:14–23

  35. Oca MA, Stutzle T (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evolut Comput 13(5):1120–1132

  36. Rao RV (2016a) Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Dec Sci Lett 5:1–30

  37. Rao RV (2016b) Teaching learning based optimization algorithm and its engineering applications. Springer, London

  38. Rao RV (2016c) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

  39. Rao RV, Patel VK (2012) An elitist teaching–learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3(4):535–560

  40. Rao RV, Saroj A (2017) A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evolut Comput. https://doi.org/10.1016/j.swevo.2017.04.008

  41. Rao RV, Waghmare GG (2014) Complex constrained design optimisation using an elitist teaching–learning-based optimisation algorithm. Int J Metaheuristic 3(1):81–102

  42. Rao RV, More KC, Taler J, Ocłoń P (2016) Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl Therm Eng 103:572–582

  43. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

  44. Runarsson TP, Xin Y (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evolut Comput 4:284–294

  45. Runarsson TP, Xin Y (2005) Search biases in constrained evolutionary optimization. IEEE Trans Syst Man Cybern C Appl Rev 35:233–243

  46. Salmani HS, Eshghi K (2017) A metaheuristic algorithm based on chemotherapy science: CSA. J Optim. https://doi.org/10.1155/2017/3082024

  47. Takahama T, Sakai S (2005) Constrained optimization by applying the constrained method to the nonlinear simplex method with mutations. IEEE Trans Evolut Comput 9(5):437–451

  48. Tessema B, Yen GG (2006) A self-adaptive penalty function based algorithm for constrained optimization. In: IEEE congress on evolutionary computation, pp 246–253

  49. Wang Y, Cai Z, Zhou Y, Fan Z (2009) Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint handling technique. Struct multidiscip Optim 37:395–413

  50. Yang S, Li C (2010) A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Trans Evolut Comput 14(6):959–974

  51. Zahara E, Kao YT (2009) Hybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Syst Appl 36:3880–3886

  52. Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178:3043–3074

Download references

Author information

Correspondence to R. Venkata Rao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rao, R.V., Saroj, A. An elitism-based self-adaptive multi-population Jaya algorithm and its applications. Soft Comput 23, 4383–4406 (2019). https://doi.org/10.1007/s00500-018-3095-z

Download citation

Keywords

  • Multi-population Jaya algorithm
  • CEC 2015
  • Heat sink