Paper Info
Title
Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms
Abstract
All dynamic crop models for growth and development have several parameters whose values are usually determined by using measurements coming from the real system. The parameter estimation problem is raised as an optimization problem and optimization algorithms are used to solve it. However, because the model generally is nonlinear the optimization problem likely is multimodal and therefore classical local search methods fail in locating the global minimum and as a consequence the model parameters could be inaccurate estimated. This paper presents a comparison of several evolutionary (EAs) and bio-inspired (BIAs) algorithms, considered as global optimization methods, such as Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) on parameter estimation of crop growth SUCROS (a Simple and Universal CROp Growth Simulator) model. Subsequently, the SUCROS model for potential growth was applied to a husk tomato crop (Physalis ixocarpa Brot. ex Horm.) using data coming from an experiment carried out in Chapingo, Mexico. The objective was to determine which algorithm generates parameter values that give the best prediction of the model. An analysis of variance (ANOVA) was carried out to statistically evaluate the efficiency and effectiveness of the studied algorithms. Algorithm's efficiency was evaluated by counting the number of times the objective function was required to approximate an optimum. On the other hand, the effectiveness was evaluated by counting the number of times that the algorithm converged to an optimum. Simulation results showed that standard DE/rand/1/bin got the best result.
Year
DOI
Venue
2014
10.1016/j.asoc.2014.06.023
Appl. Soft Comput.
Keywords
DocType
Volume
bio-inspired algorithms,evolutionary algorithms,parameter estimation,sucros model
Journal
23,
Issue
ISSN
Citations 
7
1568-4946
0
PageRank 
References 
Authors
0.34
11
5
Name
Order
Citations
PageRank
Elmer César Trejo Zúñiga100.34
Irineo Lorenzo López Cruz200.34
Agustín Ruíz García300.34
López CruzIrineo Lorenzo400.34
GarcíaAgustín Ruíz500.34