The highest cost production of the poultry industry is the feed that given to the poultry on daily basis. Unfortunately, manual formulation of poultry diet becomes difficult task when several nutritional requirements with fluctuating price are accounted. Several evolutionary approaches have been employed to solve this complex problem such as particle swarm optimization (PSO). However, in order to prevent premature convergence, PSO highly depends on the diversity of particles that influenced by acceleration component. This study presents a strategy to improve diversity in PSO using two swarms with migration and learning phase (PSO-2S). Numerical experimental results show that swarm size of 20 for each swarm, total iteration of migration phase of 42,000, and total iteration of learning phase of 40,000 are the good choice parameter of PSO-2S. While comparison experimental results show that PSO-2S can provide good solutions with the lowest cost and standard deviation than genetic algorithm, canonical PSO, and another migration strategy in multi-swarm PSO.
Optimizing Laying Hen Diet Using Particle Swarm Optimization with Two Swarms
2018-02-05
Journal of Telecommunication, Electronic and Computer Engineering (JTEC); Vol 10, No 1-6: Breakthrough To Excellence in Communication and Computer Engineering III; 113-119 ; 2289-8131 ; 2180-1843
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Optimizing Laying Hen Diet using Multi-Swarm Particle Swarm Optimization
BASE | 2018
|IoD swarms collision avoidance via improved particle swarm optimization
Elsevier | 2020
|Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms
BASE | 2021
|Optimizing Robot Path Planning with the Particle Swarm Optimization Algorithm
BASE | 2023
|Optimizing ship energy efficiency: Application of particle swarm optimization algorithm
SAGE Publications | 2018
|