Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations

Hybridizing of the optimization algorithms provides a scope to improve the searching abilities of the resulting method. The purpose of this paper is to develop a novel hybrid optimization algorithm entitled hybrid robust differential evolution (HRDE) by adding positive properties of the Taguchi's method to the differential evolution algorithm for minimizing the production cost associated with multi-pass turning problems. The proposed optimization approach is applied to two case studies for multi-pass turning operations to illustrate the effectiveness and robustness of the proposed algorithm in machining operations. The results reveal that the proposed hybrid algorithm is more effective than particle swarm optimization algorithm, immune algorithm, hybrid harmony search algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing and Hooke-Jeevespatter search.

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► A new optimization method (HRDE) based on Taguchi's method and differential evolution algorithm is developed.
► Taguchi's method is used to find appropriate interval levels of design variables.
► The HRDE is applied to the manufacturing optimization problems.
► The HRDE gives an effective and robust way to select optimal machining parameters in turning operations.