Cost optimization of mixed feeds with the particle swarm optimization method

• Published in: Neural computing & applications Vol. 22; no. 2; pp. 383 - 390
• Main Authors: Altun, Adem Alpaslan, Şahman, Mehmet Akif
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01-02-2013; Springer
• Subjects: Applied sciences; Artificial Intelligence; Calculus of variations and optimal control; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Learning and adaptive systems; Mathematical analysis; Mathematics; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in mathematical programming, optimization and calculus of variations; Numerical methods in optimization and calculus of variations; Numerical methods in probability and statistics; Original Article; Probability and Statistics in Computer Science; Sciences and techniques of general use; Mixed feed; Linear programming; Real-coded genetic algorithm; Particle swarm optimization; Cost optimization; Neural computation; Genetic algorithm; Animal; Optimization method; Neural network; Optimization
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-011-0701-8

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.