TY - JOUR N2 - The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO) and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time. L1 - http://journals.pan.pl/Content/101917/PDF/afe-2016-0073.pdf L2 - http://journals.pan.pl/Content/101917 PY - 2016 IS - No 3 DO - 10.1515/afe-2016-0073 KW - genetic algorithm KW - Squeeze casting process KW - Multi-objective optimization KW - Particle swarm optimization and multi-objectiveparticle swarm optimization based on crowding distance (MOPSO-CD). A1 - Patel, G.C.M. A1 - Krishna, P. A1 - Vundavilli, P.R. A1 - Parappagoudar, M.B. PB - The Katowice Branch of the Polish Academy of Sciences DA - 2016 T1 - Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization UR - http://journals.pan.pl/dlibra/publication/edition/101917 T2 - Archives of Foundry Engineering ER -