TY - GEN
T1 - A comparison of continuous genetic algorithm and particle swarm optimization in parameter estimation of Gompertz growth model
AU - Windarto,
AU - Eridani,
AU - Purwati, Utami Dyah
N1 - Publisher Copyright:
© 2019 Author(s).
PY - 2019/3/22
Y1 - 2019/3/22
N2 - Genetic algorithm and Particle Swarm Optimization are heuristic optimization methods inspired by genetic principles and swarm behavior phenomena, respectively. Those two methods are initiated by random generation of initial populations (initial solutions), fitness evaluation of every solution, solution updating until a termination condition are met. It is well known that those two methods are not always converge to an optimal solution. Those methods sometimes converge to suboptimal solutions, solution near the optimal solution. In this paper, continuous genetic algorithm and particle swarm optimization were implemented to estimate parameters in the Gompertz growth model from rooster weight data cited from literature. Although the best results of the two models were not significantly differs, we found that the particle swarm optimization method was more robust than the continuous genetic algorithm. Hence, the particle swarm optimization method is more recommended than the continuous genetic algorithm.
AB - Genetic algorithm and Particle Swarm Optimization are heuristic optimization methods inspired by genetic principles and swarm behavior phenomena, respectively. Those two methods are initiated by random generation of initial populations (initial solutions), fitness evaluation of every solution, solution updating until a termination condition are met. It is well known that those two methods are not always converge to an optimal solution. Those methods sometimes converge to suboptimal solutions, solution near the optimal solution. In this paper, continuous genetic algorithm and particle swarm optimization were implemented to estimate parameters in the Gompertz growth model from rooster weight data cited from literature. Although the best results of the two models were not significantly differs, we found that the particle swarm optimization method was more robust than the continuous genetic algorithm. Hence, the particle swarm optimization method is more recommended than the continuous genetic algorithm.
KW - Gompertz growth model
KW - parameter estimation
KW - particle swarm optimization
KW - rooster weight dynamic
UR - http://www.scopus.com/inward/record.url?scp=85063894077&partnerID=8YFLogxK
U2 - 10.1063/1.5094281
DO - 10.1063/1.5094281
M3 - Conference contribution
AN - SCOPUS:85063894077
T3 - AIP Conference Proceedings
BT - Proceedings of the Symposium on BioMathematics, SYMOMATH 2018
A2 - Handari, Bevina Desjwiandra
A2 - Seno, Hiromi
A2 - Tasman, Hengki
PB - American Institute of Physics Inc.
T2 - International Symposium on BioMathematics 2018, SYMOMATH 2018
Y2 - 31 August 2018 through 2 September 2018
ER -