Numerical methods for initialization in fodder composition optimization

Abstract

Determining the fodder composition is one of the important things to be done in animal raising because it may affect production. The process of determining the fodder composition is difficult to do because there are many things that must be considered at the same time, for example, the necessity to fulfill the nutrient needs while minimizing the total cost of the feed ingredients used. Evolutionary algorithms are often used to optimize the composition of animal feed with a random initial value. In this study, the use of numerical methods such as Cramer's Rule, Gauss-Elimination and Gauss-Jordan method is used as a solution for determining the initial value in evolutionary algorithms. The initial value which calculated using these three methods is the coefficient values that describe the amount of feed ingredients used in mixing fodder. The results showed that Cramer's Rule is better than Gauss-Elimination and Gauss-Jordan method with the difference in value of 7 × 10−13.