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A comparative study on multi-objective pareto optimization of WEDM process using nature-inspired metaheuristic algorithms

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Abstract

Due to its ability to generate complex and intricate shape features on different hard-to-cut materials with minimum dimensional deviation and higher surface finish, wire electrical discharge machining (WEDM) process has already become well-acknowledged in various modern-day manufacturing industries. For optimizing the machining performance of this process, applications of numerous metaheuristic algorithms and multi-criteria decision making (MCDM) techniques are already available in the existent literature. In this paper, six recently developed but yet to be popular nature-inspired metaheuristic algorithms, i.e. multi-objective ant lion optimization (MOALO) algorithm, multi-objective dragonfly algorithm (MODA), multi-objective grasshopper optimization algorithm (MOGOA), multi-objective grey wolf optimizer (MOGWO), non-dominated sorting moth flame optimization (NSMFO) algorithm and non-dominated sorting whale optimization algorithm (NSWOA) are employed for Pareto optimization of a WEDM process. From the developed Pareto optimal fronts, the optimal parametric combinations of the said process are extracted with the help of a post priori MCDM tool called combined compromise solution (CoCoSo) method. It is revealed that for all the considered nature-inspired metaheuristics, the optimal parametric intermixes vary with respect to the weights assigned to the responses. However, MOGWO, MOGOA and MODA are able to identify the best solutions 47%, 28% and 20% of the cases respectively.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and Technology, Avadi, India

    Kanak Kalita

  2. Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Rangpo, India

    Ranjan Kumar Ghadai

  3. Production Engineering Department, Jadavpur University, Kolkata, India

    Shankar Chakraborty

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  1. Kanak Kalita
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  2. Ranjan Kumar Ghadai
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  3. Shankar Chakraborty
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Correspondence to Shankar Chakraborty.

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Fig. 12
figure 12

Pareto fronts developed by the six metaheuristics

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Kalita, K., Ghadai, R.K. & Chakraborty, S. A comparative study on multi-objective pareto optimization of WEDM process using nature-inspired metaheuristic algorithms. Int J Interact Des Manuf 17, 499–516 (2023). https://doi.org/10.1007/s12008-022-01007-8

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