遗传算法的多目标分式规划

D. Roy, R. Dasgupta
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引用次数: 1

摘要

任何系统或组织的效率都可以用产出除以投入来表示。如果一个组织有多个输入,有效的输入可以看作是输入的线性组合,同样,输出也可以看作是输出的组合。输出的线性组合除以输入的比率是一个分数。这个多变量分数的优化是一个数学挑战。一个系统可能有多个这样的比率需要优化,其中自变量在所有分数函数中是相同的。虽然有大量的数值算法来求解这种异常函数,但遗传算法的性能要好得多。本文利用MATLAB实现的遗传算法,给出了求解多分式多目标优化问题的Pareto最优前沿的一种新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi objective fractional programming by genetic algorithm
Efficiency of any system or organization can be dealt as output divided by input. In case an organization has multiple inputs, the effective input can be treated as a linear combination of inputs and similarly output can be treated as a combination of outputs. This ratio of the linear combination of output divided by input is a fraction. Optimization of this multivariable fraction is a mathematical challenge. A system may have multiple such ratios to be optimized, where independent variables are same in all the fractional functions. Though there is a large number of numerical algorithms for solving such an abnormal function, it has been found genetic algorithm performs far better. In this paper a new way of obtaining the Pareto Optimal front for the Multi Objective Optimisation problem consisting of multiple fractions has been demonstrated using Genetic Algorithm implemented in MATLAB.
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