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The drilling process based on Material Reduction Rate (MRR) is modeled in this work. The modeling of this process is rather time-consuming and expensive as it involves 32 experiments with appropriate apparatus. Having had the model, the authors employed the well-known algorithm, namely Particle Swarm Optimization (PSO) to solve the maximization problem with some constraints present. All the results obtained showed nonviolation to the constraints imposed. It means the solutions found are all feasible. The developed program may be useful for some practical purposes such as estimating the drilling duration, proper time to change the drill etc.

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In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.