Advertisement

Jaya Optimization Algorithm and Its Variants

  • Ravipudi Venkata RaoEmail author
Chapter

Abstract

This chapter presents the details of TLBO algorithm, NSTLBO algorithm, Jaya algorithm and its variants named as Self-Adaptive Jaya, Quasi-Oppositional Jaya, Self-Adaptive Multi-Population Jaya, Self-Adaptive Multi-Population Elitist Jaya, Chaos Jaya, Multi-Objective Jaya, and Multi-Objective Quasi-Oppositional Jaya. Suitable examples are included to demonstrate the working of Jaya algorithm and its variants for the unconstrained and constrained single and multi-objective optimization problems. Three performance measures of coverage, spacing and hypervolume are also described to assess the performance of the multi-objective optimization algorithms.

References

  1. Beume, N., Fonseca, C. M., Manuel, L.-I., Paquete, L., & Vahrenhold, J. (2009). On the complexity of computing the hypervolume indicator. IEEE Transactions on Evolutionary Computation, 13(5), 1075–1082.CrossRefGoogle Scholar
  2. Jiang, S., Zhang, J., Ong, Y.-S., Zhang, A. N., & Tan, P. S. (2015). A simple and fast hypervolume indicator-based multiobjective evolutionary algorithm. IEEE Transactions on Cybernetics, 45(10), 2202–2213.CrossRefGoogle Scholar
  3. Rao, R. V. (2016a). Teaching learning based optimization algorithm and its engineering applications. Switzerland: Springer.CrossRefGoogle Scholar
  4. Rao, R. V. (2016b). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7, 19–34.Google Scholar
  5. Rao, R. V., & More, K. (2017a). Design optimization and analysis of selected thermal devices using self-adaptive Jayaalgorithm. Energy Conversion and Management, 140, 24–35.CrossRefGoogle Scholar
  6. Rao, R. V., & More, K. (2017b). Optimal design and analysis of mechanical draft cooling tower using improved Jaya algorithm. International Journal of Refrigeration.  https://doi.org/10.1016/j.ijrefrig.2017.06.024.CrossRefGoogle Scholar
  7. Rao, R. V., & Rai, D. P. (2017a). Optimization of welding processes using quasi oppositional based Jaya algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 29(5), 1099–1117.CrossRefGoogle Scholar
  8. Rao, R. V., & Rai, D. P. (2017b). Optimization of submerged arc welding process using quasi-oppositional based Jaya algorithm. Journal of Mechanical Science and Technology, 31(5), 1–10.CrossRefGoogle Scholar
  9. Rao, R. V., Rai, D. P., Balic, J. (2016). Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–Learning-based optimization algorithm. Journal of Intelligent Manufacturing, 2016.  https://doi.org/10.1007/s10845-016-1210-5.
  10. Rao, R. V., Rai, D. P., & Balic, J. (2017). A multi-objective algorithm for optimization of modern machining processes. Engineering Applications of Artificial Intelligence, 61, 103–125.CrossRefGoogle Scholar
  11. Rao, R. V., & Saroj, A. (2017). A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm and Evolutionary Computation.  https://doi.org/10.1016/j.swevo.2017.04.008.CrossRefGoogle Scholar
  12. Rao, R. V., & Saroj, A. (2018). An elitism-based self-adaptive multi-population Jaya algorithm and its applications. Soft Computing. https://doi.org/10.1007/s00500-018-3095-z.
  13. 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, 303–315.CrossRefGoogle Scholar
  14. Simon, D. (2013). Evolutionary optimization algorithms. New York: Wiley.Google Scholar
  15. Teo, T. (2006). Exploring dynamic self-adaptive populations in differential evolution. Soft Computing, 10, 673–686.CrossRefGoogle Scholar
  16. Yang, S. H., & Natarajan, U. (2010). Multiobjective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches. International Journal of Advanced Manufacturing Technology, 49, 773–784.CrossRefGoogle Scholar
  17. Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32–49.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