Paper Title
A Review on Evolutionary Optimization Techniques for Optimizing Various Machining Parameters

Abstract
In today�s era of growing competition in manufacturing sector, sustainability and growth depend to a large extent on higher product quality along with constraints of lower cost and lead time. One of the several ways to help in achieving these goals is through optimizing various machining process parameters such as cutting speed, feed, depth of cut, tool life, cutting forces and surface finish. Over the last few decades, evolutionary optimization techniques have become more popular for optimization of machining process parameters. This paper presents a review of various researches that have used evolutionary optimization techniques to optimize machining process parameters of both traditional and modern machining. Five optimization techniques used frequently by researchers have been discussed, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Keywords - machining process parameters, evolutionary optimization techniques, genetic algorithm, simulated annealing, particle swarm optimization, ant colony optimization, artificial bee colony algorithm.