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Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method

Author

Listed:
  • R. Venkata Rao

    (S. V. National Institute of Technology)

  • Dhiraj P. Rai

    (S. V. National Institute of Technology)

  • J. Balic

    (University of Maribor)

Abstract

In this work, the process parameters optimization problems of abrasive waterjet machining process are solved using a recently proposed metaheuristic optimization algorithm named as Jaya algorithm and its posteriori version named as multi-objective Jaya (MO-Jaya) algorithm. The results of Jaya and MO-Jaya algorithms are compared with the results obtained by other well-known optimization algorithms such as simulated annealing, particle swam optimization, firefly algorithm, cuckoo search algorithm, blackhole algorithm and bio-geography based optimization. A hypervolume performance metric is used to compare the results of MO-Jaya algorithm with the results of non-dominated sorting genetic algorithm and non-dominated sorting teaching–learning-based optimization algorithm. The results of Jaya and MO-Jaya algorithms are found to be better as compared to the other optimization algorithms. In addition, a multi-objective decision making method named PROMETHEE method is applied in this work in order to select a particular solution out-of the multiple Pareto-optimal solutions provided by MO-Jaya algorithm which best suits the requirements of the process planer.

Suggested Citation

  • R. Venkata Rao & Dhiraj P. Rai & J. Balic, 2019. "Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2101-2127, June.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:5:d:10.1007_s10845-017-1373-8
    DOI: 10.1007/s10845-017-1373-8
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    References listed on IDEAS

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    1. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    2. Lean Yu & Zebin Yang & Ling Tang, 2016. "Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 423-451, March.
    3. Mohamed Arezki Mellal & Edward J. Williams, 2016. "Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 927-942, October.
    4. Ivanoe De Falco & Umberto Scafuri & Ernesto Tarantino, 2016. "Optimizing Personalized Touristic Itineraries by a Multiobjective Evolutionary Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1269-1312, November.
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    Cited by:

    1. Elango Natarajan & Varadaraju Kaviarasan & Wei Hong Lim & Sew Sun Tiang & S. Parasuraman & Sangeetha Elango, 2020. "Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 911-935, April.
    2. Daniele Marini & Jonathan R. Corney, 2021. "Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 611-631, February.
    3. Lixia Zhu & Zeqiang Zhang & Yi Wang & Ning Cai, 2020. "On the end-of-life state oriented multi-objective disassembly line balancing problem," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1403-1428, August.

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