Multiobjective optimization of green sand mould system using DE and GSA

T. Ganesan, P. Vasant, I. Elamvazuthi, K. Shaari
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引用次数: 3

Abstract

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.
基于DE和GSA的绿砂型系统多目标优化
最近的大多数优化案例都是在多目标(MO)设置中出现的。因此,对于决策者(DM)来说,在选择最佳解决方案之前掌握多个解决方案是至关重要的。在这项工作中,加权和标量化方法与两种元启发式算法结合使用;差分进化(DE)和引力搜索算法(GSA)。然后利用这些方法生成绿砂型系统问题的近似Pareto边界。然后将本工作中的算法与先前工作中的算法进行了一些比较研究。本文给出了对这些算法的性能和解的质量的检验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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