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

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## 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.

## Keywords

Multiobjective decision making Abrasive waterjet machining process Jaya algorithm PROMETHEE Hypervolume## Notes

### Acknowledgements

The Authors are thankful to the Department of Science and Technology (DST), India and the Slovenian Research Agency (ARRS), Slovenia for providing the financial support for the project entitled “Optimization of Sustainable Advanced Manufacturing Processes”.

## References

- Aydin, G., Karakurt, I., & Hamzacebi, C. (2014). Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting.
*International Journal of Advanced Manufacturing Technology*,*75*, 1321–1330.CrossRefGoogle Scholar - Chandrasekaran, M., Muralidhar, M., Krishna, C. M., & Dixit, U. S. (2010). Application of soft computing techniques in machining performance prediction and optimization? A literature review.
*International Journal of Advanced Manufacturing Technology*,*46*, 445–464.CrossRefGoogle Scholar - Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II.
*IEEE Transaction on Evolutionary Computation*,*6*, 182–197.CrossRefGoogle Scholar - Ergur, H. S., & Oysal, Y. (2015). Estimation of cutting speed in abrasive water jet using an adaptive wavelet neural network.
*Journal of Intelligent Manufacturing*,*26*, 403–413.CrossRefGoogle Scholar - Falco, I. D., Scafuri, U., & Tarantino, E. (2016). Optimizing personalized touristic itineraries by a multiobjective evolutionary algorithm.
*International Journal of Information Technology & Decision Making*,*15*, 1269–1312.Google Scholar - Huang, J., Gao, L., & Li, X. (2015). An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes.
*Applied Soft Computing*,*36*, 349–356.CrossRefGoogle Scholar - Jagadish, B. S., & Ray, A. (2016). Prediction and optimization of process parameters of green composites in AWJM process using response surface methodology.
*International Journal of Advanced Manufacturing Technology*,*87*, 1359–1370.CrossRefGoogle Scholar - Jagadish, B. S., & Ray, A. (2015). Prediction of surface roughness quality of green abrasive water jet machining: A soft computing approach.
*Journal of Intelligent Manufacturing*. https://doi.org/10.1007/s10845-015-1169-7. - Jain, N. K., Jain, V. K., & Deb, K. (2007). Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms.
*International Journal of Machine Tools & Manufacture*,*47*, 900–919.CrossRefGoogle Scholar - Jegaraj, J. J. R., & Babu, N. R. (2007). A soft computing approach for controlling the quality of cut with abrasive waterjet cutting system experiencing orifice and focusing tube wear’.
*Journal of Materials Processing Technology*,*185*, 217–227.CrossRefGoogle Scholar - Kechagias, J., Petropoulos, G., & Vaxevanidis, N. (2012). Application of Taguchi design for quality characterization of abrasive water jet machining of TRIP sheet steels.
*International Journal of Advanced Manufacturing Technology*,*62*, 635–643.CrossRefGoogle Scholar - Kok, M., Kanca, E., & Eyercioglu, O. (2011). Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming.
*International Journal of Advanced Manufacturing Technology*,*55*, 955–968.CrossRefGoogle Scholar - Liu, D., Huang, C., Wang, J., Zhu, H., Yao, P., & Liu, Z. W. (2014). Modeling and optimization of operating parameters for abrasive waterjet turning alumina ceramics using response surface methodology combined with Box-Behnken design.
*Ceramics International*,*40*, 7899–7908.CrossRefGoogle Scholar - Mellal, M. A., & Williams, E. J. (2016). Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic.
*Journal of Intelligent Manufacturing*,*27*, 927–942.CrossRefGoogle Scholar - Mohamad, A., Zain, A. M., Bazin, N. E. N., & Udin, A. (2015). A process prediction model based on Cuckoo algorithm for abrasive waterjet machining.
*Journal of Intelligent Manufacturing*,*26*, 1247–1252.CrossRefGoogle Scholar - Parikh, P. J., & Lam, S. S. (2009). Parameter estimation for abrasive water jet machining process using neural networks.
*International Journal of Advanced Manufacturing Technology*,*40*, 497–502.CrossRefGoogle Scholar - Pawar, P. J., & Rao, R. V. (2013). Parameter optimization of machining processes using teaching-learning-based optimization algorithm.
*International Journal of Advanced Manufacturing Technology*,*67*, 995–1006.CrossRefGoogle Scholar - Rao, R. V. (2011).
*Advancned modeling and optimization of manufacturing processes*. London: Springer.CrossRefGoogle Scholar - Rao, R. V. (2015).
*Teaching learning based optimization algorithm and its engineering applications*. Cham: Springer.Google Scholar - 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*, 19–34.Google Scholar - Rao, R. V., & Kalyankar, V. D. (2014). Optimization of modern machining processes using advanced optimization techniques: A review.
*International Journal of Advanced Manufacturing Technology*,*73*, 1159–1188.CrossRefGoogle Scholar - Rao, R. V., & Patel, B. K. (2010). Decision making in the manufacturing environment using an improved PROMETHEE method.
*International Journal of Production Research*,*48*, 4665–4682.CrossRefGoogle Scholar - Rao, R. V., & Waghmare, G. G. (2016). A new optimization algorithm for solving complex constrained design optimization problems.
*Engineering Optimization*. https://doi.org/10.1080/0305215X.2016.1164855. - 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*. https://doi.org/10.1007/s10845-016-1210-5. - 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 - Rostami, S., & Neri, F. (2017). A fast hypervolume driven selection mechanism for many-objective optimisation problems.
*Swarm and Evolutionary Computation*,*34*, 50–67.CrossRefGoogle Scholar - Santhanakumar, M., Adalarasan, R., & Rajmohan, M. (2015). Experimental modelling and analysis in abrasive waterjet cutting of ceramic tiles using grey-based response surface methodology.
*Arabian Journal of Science and Engineering*,*40*, 3299–3311.CrossRefGoogle Scholar - Santhanakumar, M., Adalarasan, R., & Rajmohan, M. (2016). Parameter design for cut surface characteristics in abrasive waterjet cutting of Al/SiC/Al\(_{2}\)O\(_{3}\) composite using grey theory based RSM.
*Journal of Mechanical Science and Technology*,*30*, 371–379.CrossRefGoogle Scholar - Shanmugam, D. K., Wang, J., & Liu, H. (2008). Minimisation of kerf tapers in abrasive waterjet machining of alumina ceramics using a compensation technique.
*International Journal of Machine Tools and Manufacture*,*48*, 1527–1534.CrossRefGoogle Scholar - Shukla, R., & Singh, D. (2016a). Experimentation investigation of abrasive water jet machining parameters using Taguchi and Evolutionary optimization techniques.
*Swarm and Evolutionary Computation*. https://doi.org/10.1016/j.swevo.2016.07.002. - Shukla, R., & Singh, D. (2016b). Selection of parameters for advanced machining processes using firefly algorithm.
*Engineering Science and Technology: An International Journal*. https://doi.org/10.1016/j.jestch.2016.06.001. - Srinivasu, D. S., & Babu, N. R. (2008). An adaptive control strategy for the abrasive waterjet cutting process with the integration of vision-based monitoring and a neuro-genetic control strategy.
*International Journal of Advanced Manufacturing Technology*,*38*, 514–523.CrossRefGoogle Scholar - Vundavilli, P. R., Parappagoudar, M. B., Kodali, S. P., & Benguluri, S. (2012). Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process.
*Knowledge-Based Systems*,*27*, 456–464.CrossRefGoogle Scholar - Yu, L., Yang, Z., & Tang, L. (2016). Prediction based multi-objective optimization for oil purchasing and distribution with the NSGA-II algorithm.
*International Journal of Information Technology & Decision Making*,*15*, 423–451.Google Scholar - Yue, Z., Huang, C., Zhu, H., Wang, J., Yao, P., & Liu, Z. W. (2014). Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology.
*International Journal of Advanced Manufacturing Technology*,*71*, 2107–2114.CrossRefGoogle Scholar - Yusup, N., Sarkheyli, A., Zain, A. M., Hashim, S. Z. M., & Ithnin, N. (2014). Estimation of optimal machining control parameters using artificial bee colony.
*Journal of Intelligent Manufacturing*,*25*, 1463–1472.CrossRefGoogle Scholar - Yusup, N., Zain, A. M., & Hashim, S. Z. M. (2012). Evolutionary techniques in optimizing machining parameters: Review and recent applications.
*Expert Systems with Applications*,*39*, 9909–9927.CrossRefGoogle Scholar - Zain, A. M., Haron, H., & Sharif, S. (2011a). Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA.
*Expert Systems with Applications*,*38*, 8316–8326.CrossRefGoogle Scholar - Zain, A. M., Haron, H., & Sharif, S. (2011b). Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA.
*Applied Soft Computing*,*11*, 5350–5359.CrossRefGoogle Scholar - Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. (2011). Multi-objective evolutionary algorithms: A survey of the state of the art.
*Swarm and Evolutionary Computation*,*1*, 32–49.CrossRefGoogle Scholar - Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach.
*IEEE Transactions on Evolutionary Computation*,*3*, 257–271.CrossRefGoogle Scholar - Zohoor, M., & Nourian, S. H. (2012). Development of an algorithm for optimum control process to compensate the nozzle wear effect in cutting the hard and tough material using abrasive water jet cutting process.
*International Journal of Advanced Manufacturing Technology*,*61*, 1019–1028.CrossRefGoogle Scholar