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Performance Analysis of Jaya Algorithm Using CEC’2013 Benchmark Functions

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1053))

Abstract

Jaya algorithm is population-based parameter less heuristic algorithm. The algorithm requires only control parameters like population size and a number of iterations and two stochastic steps (three random number generators). This paper presents an investigation of Jaya algorithm on Congress on Evolutionary Computation 2013 test functions. The experimental results show that Jaya is performing satisfactorily for 28 benchmark functions for lower dimensions and performance degrades for higher dimensions.

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References

  1. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3), 303–315 (2011)

    Article  Google Scholar 

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

    Google Scholar 

  3. Chen, D., Zou, F., Li, Z., Wang, J., Li, S.: An improved teaching–learning-based optimization algorithm for solving global optimization problem. Inf. Sci. 297, 171–190 (2015)

    Article  Google Scholar 

  4. Roy, P.K.: Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint. Int. J. Electr. Power Energy Syst. 53, 10–19 (2013)

    Article  Google Scholar 

  5. Niknam, T., Azizipanah-Abarghooee, R., Aghaei, J.: A new modified teaching-learning algorithm for reserve constrained dynamic economic dispatch. IEEE Trans. Power Syst. 28(2), 749–763 (2013)

    Article  Google Scholar 

  6. Baykasoğlu, A., Hamzadayi, A., Köse, S.Y.: Testing the performance of teaching–learning based optimization (TLBO) algorithm on combinatorial problems: flow shop and job shop scheduling cases. Inf. Sci. 276, 204–218 (2014)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  8. Mishra, S., Ray, P.K.: Power quality improvement using photovoltaic fed DSTATCOM based on JAYA optimization. IEEE Trans. Sustainable Energy 7(4), 1672–1680 (2016)

    Article  Google Scholar 

  9. Azizipanah-Abarghooee, R., Dehghanian, P., Terzija, V.: Practical multi-area bi-objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm. IET Gener. Transm. Distrib. 10(14), 3580–3596 (2016)

    Article  Google Scholar 

  10. Degertekin, S.O., Lamberti, L., Ugur, I.B.: Sizing, layout and topology design optimization of truss structures using the Jaya algorithm. Appl. Soft Comput. 70, 903–928 (2018)

    Article  Google Scholar 

  11. Singh, S.P., Prakash, T., Singh, V.P., Babu, M.G.: Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm. Eng. Appl. Artif. Intell. 60, 35–44 (2017)

    Article  Google Scholar 

  12. Azizipanah-Abarghooee, R., Golestaneh, F., Gooi, H.B., Lin, J., Bavafa, F., Terzija, V.: Corrective economic dispatch and operational cycles for probabilistic unit commitment with demand response and high wind power. Appl. Energy 182, 634–651 (2016)

    Article  Google Scholar 

  13. Buddala, R., Mahapatra, S.S.: Improved teaching–learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems. J. Ind. Eng. Int. 1–16 (2017)

    Google Scholar 

  14. Theologi, A.M., Ndreko, M., Rueda, J.L., Van Der Meijden, M.A.M.M., González-Longatt, F.: Optimal management of reactive power sources in far-offshore wind power plants. In: PowerTech, 2017 IEEE Manchester, pp. 1–6 (2017). IEEE

    Google Scholar 

  15. Liang, J., Qu, B., Suganthan, P.N., Hernández-Díaz, A.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. In: Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report 2013, vol. 201212, pp. 3–18 (2013)

    Google Scholar 

  16. Zambrano-Bigiarini, M., Clerc, M., Rojas, R.: Standard particle swarm optimization 2011 at CEC-2013: a baseline for future PSO improvements. In: 2013 IEEE Congress on Evolutionary Computation (CEC 2013), pp. 2337–2344 (2013)

    Google Scholar 

  17. Umbarkar, A.J., Moon, L.R., Sheth, P.D.: Comparative study of CEC’2013 problem using dual population genetic algorithm. Int. J. Inf. Eng. Electron. Bus. (IJIEEB) 10(5), 40–45 (2018). https://doi.org/10.5815/ijieeb.2018.05.06

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Author information

Authors and Affiliations

  1. Walchand College of Engineering, Sangli, Maharashtra, India

    A. J. Umbarkar & S. B. Darade

  2. Rajarambapu Institute of Technology, Rajaramnagar, Maharashtra, India

    A. C. Adamuthe

Authors
  1. A. J. Umbarkar
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  2. A. C. Adamuthe
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  3. S. B. Darade
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Corresponding author

Correspondence to A. J. Umbarkar .

Editor information

Editors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  3. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Om Prakash Verma

  4. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Rajesh Singla

  5. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Afzal Sikander

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Umbarkar, A.J., Adamuthe, A.C., Darade, S.B. (2020). Performance Analysis of Jaya Algorithm Using CEC’2013 Benchmark Functions. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_56

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