Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.