Application of Jaya Algorithm and Its Variants on Constrained and Unconstrained Benchmark Functions

  • Ravipudi Venkata RaoEmail author


This chapter presents the results of application of Jaya algorithm and its variants like SAMP-Jaya and SAMPE-Jaya algorithms on 15 unconstrained benchmark functions given in CEC 2015 as well as 15 other unconstrained functions and 5 constrained benchmark functions. The results are compared with those given by the other well known optimization algorithms. The results have shown the satisfactory performance of Jaya algorithm and its variants for the considered CEC 2015 benchmark functions and the other constrained and unconstrained optimization problems. The statistical tests have also supported the performance supremacy of the variants of the Jaya algorithm.


Jaya Algorithm Unconstrained Benchmark Problems Fully Informed Particle Swarm (FIPS) Particle Swarm Optimization Learning Algorithm Maximum Function Evaluations 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Andersson, M., Bandaru, S., Ng, A. H. C., & Syberfeldt, A. (2015). Parameter tuned CMA-ES on the CEC’15 expensive problems. In IEEE Congress on Evolutionary Computation. Japan: Sendai.Google Scholar
  2. Becerra, R., & Coello, C. A. C. (2006). Cultured differential evolution for constrained optimization. Computer Methods in Applied Mechanics and Engineering, 195, 4303–4322.MathSciNetCrossRefGoogle Scholar
  3. Bergh, F. V., & Engelbrecht, A. P. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 225–239.CrossRefGoogle Scholar
  4. Cheng, R., & Jin, Y. (2015a). A Competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics, 45(2), 191–204.CrossRefGoogle Scholar
  5. Cheng, R., & Jin, Y. (2015b). A social learning particle swarm optimization algorithm for scalable optimization. Information Sciences, 291, 43–60.MathSciNetCrossRefGoogle Scholar
  6. Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Sixth International Symposium on Micro machine and Human Science (pp. 39–43). Japan: Nagoya.CrossRefGoogle Scholar
  7. Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. Hoboken, New Jersey: Wiley.zbMATHGoogle Scholar
  8. Huang, F. Z., Wang, L., & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematical Computations, 186, 340–356.MathSciNetCrossRefGoogle Scholar
  9. 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 and Evolutionary Computation, 1(1), 3–18.Google Scholar
  10. Karaboga, D., & Akay, B. (2011). A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing, 11, 3021–3031.CrossRefGoogle Scholar
  11. Karaboga, D., & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. LNAI 4529 (pp. 789–798). Berlin: Springer.Google Scholar
  12. Liang, J. J., Runarsson, T. P., Mezura-Montes, E., Clerc, M., Suganthan, P. N., Coello, C. A. C., & Deb, K. (2006) Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization, Technical Report, Nanyang Technological University, Singapore.
  13. Liang, J. J., & Qin, A. K. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.CrossRefGoogle Scholar
  14. Mendes, R., Kennedy, J., & Neves, J. (2004). The fully informed particle swarm: Simpler, may be better. IEEE Transactions on Evolutionary Computation, 8(3), 204–210.CrossRefGoogle Scholar
  15. Mezura-Montes, E., & Coello, C. A. C. (2006). A simple multi membered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation, 9, 1–17.CrossRefGoogle Scholar
  16. Ngo, T. T., Sadollah, A. J., & Kim, H. (2016). A cooperative particle swarm optimizer with stochastic movements for computationally expensive numerical optimization problems. Journal of Computational Science, 13, 68–82.MathSciNetCrossRefGoogle Scholar
  17. Nickabadi, A., Ebadzadeh, M. M., & Safabakhsh, R. (2011). A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing, 11(4), 3658–3670.CrossRefGoogle Scholar
  18. Oca, M. A., & Stutzle, T. (2009). Frankenstein’s PSO: A composite particle swarm optimization algorithm. IEEE Transactions on Evolutionary Computation, 13(5), 1120–1132.CrossRefGoogle Scholar
  19. Rao, R. V. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19–34.MathSciNetGoogle Scholar
  20. Rao, R. V., & Saroj, A. (2017). A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm and Evolutionary Computation. Scholar
  21. Rao, R. V., & Saroj, A. (2018). An elitism based self-adaptive multi-population Jaya algorithm and its applications. Soft Computing, 1–24.
  22. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315.CrossRefGoogle Scholar
  23. Rao, R. V., & Waghmare, G. G. (2014). Complex constrained design optimization using an elitist teaching-learning-based optimization algorithm. International Journal of Metaheuristics, 3(1), 81–102.CrossRefGoogle Scholar
  24. Rao, R. V., & Waghmare, G. G. (2017). A new optimization algorithm for solving complex constrained design optimization problems. Engineering Optimization, 49(1), 60–83.CrossRefGoogle Scholar
  25. Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.CrossRefGoogle Scholar
  26. Wu, G. (2016). Across neighbourhood search for numerical optimization. Information Sciences, 329, 597–618.CrossRefGoogle Scholar
  27. Wu, G., Mallipeddi, R., Suganthan, P. N., Wang, R., & Chen, H. (2017). Differential evolution with multi-population based ensemble of mutation strategies. Information Sciences, 329, 329–345.CrossRefGoogle Scholar
  28. Zavala, A. E. M., Aguirre, A. H., & Diharce, E. R. V. (2005). Constrained optimization via evolutionary particle swarm optimization algorithm (PESO). Proc (pp. 209–216). Washington D.C.: GECCO.Google Scholar
  29. Zhang, G., Cheng, J., Gheorghe, M., & Meng, Q. (2013). A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Applied Soft Computing, 13(3), 1528–1542.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringS.V. National Institute of TechnologySuratIndia

Personalised recommendations