A comparison of continuous genetic algorithm and particle swarm optimization in parameter estimation of Gompertz growth model

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Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the Symposium on BioMathematics, SYMOMATH 2018
EditorsBevina Desjwiandra Handari, Hiromi Seno, Hengki Tasman
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735418141
DOIs
Publication statusPublished - 22 Mar 2019
EventInternational Symposium on BioMathematics 2018, SYMOMATH 2018 - Depok, Indonesia
Duration: 31 Aug 20182 Sept 2018

Publication series

NameAIP Conference Proceedings
Volume2084
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceInternational Symposium on BioMathematics 2018, SYMOMATH 2018
Country/TerritoryIndonesia
CityDepok
Period31/08/182/09/18

Keywords

  • Gompertz growth model
  • parameter estimation
  • particle swarm optimization
  • rooster weight dynamic

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