TY - GEN
T1 - Numerical methods for initialization in fodder composition optimization
AU - Wijayaningrum, Vivi Nur
AU - Utaminingrum, Fitri
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/6
Y1 - 2017/3/6
N2 - 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.
AB - 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.
KW - Cramer's Rule
KW - fodder composition
KW - Gauss-Elimination
KW - Gauss-Jordan
KW - linear equation system
UR - https://www.scopus.com/pages/publications/85017016651
U2 - 10.1109/ICACSIS.2016.7872730
DO - 10.1109/ICACSIS.2016.7872730
M3 - Conference contribution
AN - SCOPUS:85017016651
T3 - 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
SP - 397
EP - 400
BT - 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
Y2 - 15 October 2016 through 16 October 2016
ER -