q Multiobjective Optimization of Green Sand Mould System using DE and GSA

被引：0
|
作者：
Ganesan, T. [1 ]
Vasant, P. [2 ]
Elamvazuthi, I. [2 ]
Shaari, Ku Zilati Ku [1 ]
机构：
[1] Univ Teknol PETRONAS, Dept Chem Engn, Seri Iskandar, Malaysia
[2] Univ Teknol PETRONAS, Dept Fundamental & Appl Sci, Petronas, Malaysia
来源：
2012 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA) | 2012年
关键词：
multi-objective (MO); industrial optimization; green sand mould system; weighted sum approach; differential evolution (DE); gravitational search algorithm (GSA); Hypervolume Indicator (HVI); approximate Pareto frontier;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页码：1012 / 1016
页数：5
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