%0 Conference Paper
%A Ganesan, T.
%A Vasant, P.
%A Elamvazuthi, I.
%A Shaari, K.Z.K.
%D 2012
%F scholars:2525
%K Differential Evolution; Gravitational search algorithm (GSA); Green sand mould systems; Hypervolume indicators; Industrial optimization; Multi objective; Pareto frontiers; Weighted sum approaches, Evolutionary algorithms; Heuristic algorithms; Intelligent systems; Learning algorithms; Molds; Systems analysis, Multiobjective optimization
%P 1012-1016
%R 10.1109/ISDA.2012.6416677
%T Multiobjective optimization of green sand mould system using DE and GSA
%U https://khub.utp.edu.my/scholars/2525/
%X Most optimization cases in recent times present themselves in a multi-objective (MO) setting. Hence, it is vital for the decision maker (DM) to have in hand multiple solutions prior to selecting the best solution. In this work, the weighted sum scalarization approach is used in conjunction with two meta-heuristic algorithms; differential evolution (DE) and gravitational search algorithm (GSA). These methods are then used to generate the approximate Pareto frontier to the green sand mould system problem. Some comparative studies were then carried out with the algorithms in this work and that from the previous work. Examinations on the performance and the quality of the solutions obtained by these algorithms are shown here. © 2012 IEEE.
%Z cited By 1; Conference of 2012 12th International Conference on Intelligent Systems Design and Applications, ISDA 2012 ; Conference Date: 27 November 2012 Through 29 November 2012; Conference Code:95653