Process Parameter Optimization In Multi-Pass Turning Operation Using Hybrid Firefly Swarm Algorithm

Evolutionary algorithms are the choice of many researchers for optimizing machining parameters. Even though evolutionary algorithms are commonly used for solving constrained optimization problems, however in practice sometimes they deliver only insignificant performance. The difficulty with evolutionary algorithms is that they start with random initial population and all its populations become grossly identical after a certain amount of time. Inappropriate selection of various parameters, representation, etc. of evolutionary algorithm is one of the root causes for the failure for the better performance. All these clearly illustrate the need for hybrid evolutionary approach. Hybridization is way to improve the existing approach. The objectives of this paper are i) to analyze the parametric settings of FA that leads to optimized result by apply it in multi-pass turning ii) As PSO quick in convergence to a best value, hybridize Firefly Algorithm (FA) with Particle Swarm Optimization(PSO) called Hybrid Firefly Swarm (HFS) algorithm to exploit best parameter setting in multi-pass turning iii) To explore the convergence characteristics and robustness of the Firefly Algorithm (FA) through comparisons with results of evolutionary algorithm and with the results reported in literature.