Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method
- 452 Downloads
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.
KeywordsMultiobjective decision making Abrasive waterjet machining process Jaya algorithm PROMETHEE Hypervolume
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”.
- 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
- 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.
- 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., & 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.
- 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.
- 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
- 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