Result: Cost optimization of mixed feeds with the particle swarm optimization method
Title:
Cost optimization of mixed feeds with the particle swarm optimization method
Authors:
Source:
Neural computing & applications (Print). 22(2):383-390
Publisher Information:
London: Springer, 2013.
Publication Year:
2013
Physical Description:
print, 24 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Animal, Calcul neuronal, Neural computation, computación neuronal, Méthode optimisation, Optimization method, Método optimización, Optimisation, Optimization, Optimización, Programmation linéaire, Linear programming, Programación lineal, Réseau neuronal, Neural network, Red neuronal, 49XX, 65C35, 65K10, 65Kxx, Cost optimization, Mixed feed, Particle swarm optimization, Real-coded genetic algorithm
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electronic and Computer Science Education, Technical Educational Faculty, Selcuk University, Selcuklu/Konya, Turkey
Guneysimr Vocational High School, Selcuk University, Konya, Turkey
Guneysimr Vocational High School, Selcuk University, Konya, Turkey
ISSN:
0941-0643
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Mathematics
Mathematics
Accession Number:
edscal.27659219
Database:
PASCAL Archive
Further Information
In this study, the best mixed feed was prepared by using the algorithm of particle swarm optimization (PSO) and by taking into account the breeding type and method of the poultries and various farm animals (cattle, sheep, rabbit), their needs, ages, and feeding costs and optimizing them all. Results obtained through PSO were compared through linear programming and real-coded genetic algorithm. According to the results that were obtained, PSO produces more rapid, more stable, and optimum values.