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This paper presents an overview on NSGA-II optimization techniques of machining process parameters. There are many multi objective optimization (MoGA) techniques involved in machining process parameters optimization including multi-objective genetic algorithm (MOGA), strength Pareto evolutionary algorithm (SPEA), micro genetic algorithm (Micro-GA), Pareto-archived evolution strategy (PAES), etc. This paper reviews the application of non dominated sorting genetic algorithm II (NSGA-II), classified as one of MoGA techniques, for optimizing process parameters in various machining operations. NSGA-II is a well known, fast sorting and elite multi objective genetic algorithm. Process parameters such as cutting speed, feed rate, rotational speed etc. are the considerable conditions in order to optimize the machining operations in minimizing or maximizing the machining performances. Unlike the single objective optimization technique, NSGA-II simultaneously optimizes each objective without being dominated by any other solution.
Yusoff et al. (2011) studied this question.