title: Multi-objective evolutionary optimization of biological pest control with impulsive dynamics in soybean crops.
abstract: The biological pest control in agriculture, an environment-friendly practice, maintains the density of pests below an economic injury level by releasing a suitable quantity of their natural enemies. This work proposes a multi-objective numerical solution to biological pest control for soybean crops, considering both the cost of application of the control action and the cost of economic damages. The system model is nonlinear with impulsive control dynamics, in order to cope more effectively with the actual control action to be applied, which should be performed in a finite number of discrete time instants. The dynamic optimization problem is solved using the NSGA-II, a fast and trustworthy multi-objective genetic algorithm. The results suggest a dual pest control policy, in which the relative price of control action versus the associated additional harvest yield determines the usage of either a low control action strategy or a higher one.
authors.affiliation: Department of Physics and Mathematics, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, Brazil. rodrigoc@des.cefetmg.br
authors.forename: Rodrigo T N
authors.initials: RT
authors.lastname: Cardoso
authors.forename: André R
authors.initials: AR
authors.lastname: da Cruz
authors.forename: Elizabeth F
authors.initials: EF
authors.lastname: Wanner
authors.forename: Ricardo H C
authors.initials: RH
authors.lastname: Takahashi
mesh_terms: D000465:Algorithms; D000818:Animals; D005075:Biological Evolution; D003362:Cost-Benefit Analysis / Q000379:methods; D018556:Crops, Agricultural / Q000191:economics*; D007313:Insecta; D008954:Models, Biological; D017711:Nonlinear Dynamics; D010572:Pest Control, Biological / Q000191:economics* / Q000379:methods*; D011157:Population Dynamics; D011235:Predatory Behavior; D013025:Glycine max / Q000191:economics*