Comparative Analysis of Metaheuristic Optimizers in the Performance Optimization of Wire Electric Discharge Machining Processes

  • Subhankar Saha Department of Mechanical Engineering, National Institute of Technology Silchar, India
  • Saikat Ranjan Maity Department of Mechanical Engineering, National Institute of Technology Silchar, India
  • Sudip Dey Department of Mechanical Engineering, National Institute of Technology Silchar, India
Keywords: WEDM, optimization, metaheuristic algorithms, two-sample t-test, sensitivity analysis


WEDM is an intricate process whereby improper selection of machine parameters often leads to undesirable performances. Therefore, the extraction of optimal machining parameters is pivotal for achieving better performances in WEDM. Metaheuristic optimizers have gained immense popularity due to their capability of providing global optimal solutions. The application of recently reported metaheuristic optimizers in non-traditional machining processes is rarely being explored. In light of the above, the current paper examines the use of six recently reported metaheuristic optimizers, namely the ant lion optimization (ALO), chimp optimization algorithm (ChoA), moth flame optimization (MFO), spotted hyena optimization (SHO), Harris Hawk optimization algorithm (HHO), Marine predator algorithm (MPA) to optimize WEDM performances in three WEDM processes. Particle swarm optimization (PSO) and Teaching learning-based optimization (TLBO), two well-known existing optimization approaches, are also included in this study to enable a reasonable comparison of the algorithms' performance. The algorithms are compared with parameters such as the quality of optimal solutions, convergence behavior, and average computational time. HHO algorithm is found to be robust amongst the eight competitors in terms of culminating the global optimal solution and propensity to quickly converge to the global optimal solution which corroborates the high exploration and exploitation capability of the algorithm. Therefore, HHO optimizer can be exploited in future to determine the optimal operating conditions for other manufacturing processes.


Download data is not yet available.


Abualigah, L., Shehab, M., Alshinwan, M., Mirjalili, S., & Elaziz, M.A. (2021). Ant lion optimizer: a comprehensive survey of its variants and applications. Archives of Computational Methods in Engineering, 28, 1397-1416.

Devarajaiah, D., & Muthumari, C. (2018). Evaluation of power consumption and MRR in WEDM of Ti–6Al–4V alloy and its simultaneous optimization for sustainable production. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40, 1-18.

Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software 114, 48-70. DOI:

Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A.H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Systems with Applications. 152, 113377.

Garg, M.P., Jain, A., & Bhushan, G. (2012). Modelling and multi-objective optimization of process parameters of wire electrical discharge machining using non-dominated sorting genetic algorithm-II. . Proceedings of the Institution of Mechanical Engineers, Part B, 226, 1986-2001. DOI:

Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, & M., Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.

Houssein, E.H., Mahdy, M.A., Fathy, A., & Rezk, H. (2021). A modified Marine Predator Algorithm based on opposition based learning for tracking the global MPP of shaded PV system. Expert Systems with. Applications, 183, 115253.

Juneja, M., & Nagar, S.K. (2016). Particle swarm optimization algorithm and its parameters: A review. In 2016 International Conference on Control, Computing, Communication and Materials (ICCCCM) (pp. 1-5). IEEE. DOI:

Kuriakose, S., & Shunmugam, M.S. (2005). Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm. Journal of Materials Processing Technology, 170, 133-141. DOI:

Krishna, M.M., Panda, N., & Majhi, S.K. (2021). Solving Traveling Salesman Problem using Hybridization of Rider Optimization and Spotted Hyena Optimization Algorithm, Expert Systems with Applications, 115353.

Khan, N.Z., Khan, Z.A., Siddiquee, A.N., & Chanda, A.K. (2014). Investigations on the effect of wire EDM process parameters on surface integrity of HSLA: A multi-performance characteristics optimization. Production & Manufacturing Research, 2, 501-518.

Kaur, M., Kaur, R., Singh, & N., Dhiman, G. (2021). SChoA: a newly fusion of sine and cosine with chimp optimization algorithm for HLS of data paths in digital filters and engineering applications, Engineering with Computers, 1-29.

Khishe, M., & Mosavi, M.R. (2020). Chimp optimization algorithm. Expert Systems with Applications, 149, 113338.

Lee, K.Y., & Park, J.B. (2006). Application of particle swarm optimization to economic dispatch problem: advantages and disadvantages. In 2006 IEEE PES Power Systems Conference and Exposition (pp. 188-192), IEEE. DOI:

Majumder, H., & Maity, K. (2018). Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM. Measurement, 118, 1-13. DOI:

Mandal A, Dixit, A.R., Das, A.K., & Mandal, N. (2016). Modeling and optimization of machining nimonic C-263 superalloy using multicut strategy in WEDM. Materials and Manufacturing Processes, 31, 860-868. DOI:

Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge- Based Systems. 89, 228-249. DOI:

Mirjalili, S. (2015). The ant lion optimizer, Advances in Engineerimg Software. 83, 80-98. DOI:

Mansoor, M., Mirza, A.F., & Ling, Q. (2020). Harris hawk optimization-based MPPT control for PV systems under partial shading conditions. Journal of Cleaner Production, 274, 122857.

Nayak, B.B., & Mahapatra, S.S. (2016). Optimization of WEDM process parameters using deep cryo-treated Inconel 718 as work material. Engineering Science and Technology, an International Journal, 19, 161-170. DOI:

Naik, M.K., Panda, R., Wunnava, A., Jena, B., & Abraham, A. (2021). A leader Harris hawks optimization for 2-D Masi entropy-based multilevel image thresholding. Multimedia Tools and Applications, 80, 35543-35583.

Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence., 1, 33-57. DOI:

Ramakrishnan, R., & Karunamoorthy, L. (2008). Modeling and multi-response optimization of Inconel 718 on machining of CNC WEDM process. Journal of Materials Processing Technology, 207, 343-349. DOI:

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. DOI:

Sadeghi, M., Razavi, H., Esmaeilzadeh, A., & Kolahan, F. (2011). Optimization of cutting conditions in WEDM process using regression modelling and Tabu-search algorithm. Proceedings of the Institution of Mechanical Engineers, Part B, 225, 1825-1834. DOI:

Sultana, S., Roy, P.K. (2014). Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems, International Journal of Electrical Power & Energy Systems, 63, 534-545. DOI:

Sabahno, M., Safara, F. (2021). ISHO: Improved spotted hyena optimization algorithm for phishing website detection. Multimedia Tools and Applications, 1-20.

Shehab, M., Abualigah, L., Al Hamad H., Alabool, H., Alshinwan, M., & Khasawneh, A.M.. (2021). Moth–flame optimization algorithm: variants and applications, Neural. Computing and Applications, 32, 9859-9884.

Shan, W., Qiao, Z., Heidari, A.A., Chen, H., Turabieh, H., & Teng, Y. (2021). Double adaptive weights for stabilization of moth flame optimizer: balance analysis, engineering cases, and medical diagnosis. Knowledge.-Based Systems, 214, 106728.

Saha, S., Maity, S.R., Dey, S., & Dutta, S. (2021). Modeling and combined application of MOEA/D and TOPSIS to optimize WEDM performances of A286 superalloy, Soft Computing, 25, 14697–14713.

Tonday, H.R., & Tigga, A.M. (2019). An empirical evaluation and optimization of performance parameters of wire electrical discharge machining in cutting of Inconel 718. Measurement, 140, 185-196.

Tarng, Y.S., Ma, S.C., & Chung, L.K. (1995). Determination of optimal cutting parameters in wire electrical discharge machining. International Journal of Machine Tools and Manufacture 35, 1693-1701. DOI:

Uzlu, E., Kankal, M., Akpınar, A., & Dede, T. (2014). Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm. Energy. 75, 295-303. DOI:

Wolpert, D.H., & Macready, M.G. (1997). No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation. DOI:

Yao, Y., Li, Y., Xie, D., Hu, S., Wang, C., & Li, Y. (2021). Coverage Enhancement Strategy for WSNs Based on Virtual Force-Directed Ant Lion Optimization Algorithm. IEEE Sensors Journal, (2021).

Yakout, A.H., Sabry, W., Abdelaziz, A.Y., Hasanien, H.M., AboRas. K.M., & Kotb, H. (2021). Enhancement of frequency stability of power systems integrated with wind energy using marine predator algorithm based PIDA controlled STATCOM, Alexendria Engineering Journal.

Zhu, G., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation, 217, 3166-3173. DOI:

How to Cite
Saha, S., Maity, S. R., & Dey, S. (2022). Comparative Analysis of Metaheuristic Optimizers in the Performance Optimization of Wire Electric Discharge Machining Processes. Operational Research in Engineering Sciences: Theory and Applications.