A multiobjective non-linear dynamic programming approach for optimal biological control in soy farming via NSGA-II

Abstract

The biological control of plagues in agriculture, a practice that has been growing around the world, is performed by leaving a suitable quantity of natural enemies of the plague in the farm during the finite time horizon of the farming cycle. This work proposes a multi-objective mathematical solution for the problem of optimal biological plague control for soy farmings, considering the control cost and the cost of farming damage due to plague. The system model is non-linear with impulsive control dynamics, in order to cope with the realproblem feature of control action, that should be performed in a finite number of discrete time instants. The dynamic optimization problem is solved using the NSGA-II, a fast and elitist multiobjective genetic algorithm. The results suggest a dual plague control policy, in which the relative price of control action versus the associated additional harvesting determine the usage of either a low control action or a higher well-defined one.

Solving Impulsive Control Problems by Discrete-Time Dynamic Optimization Methods

“…Biological control of pests in crops aim the reduction of pest populations, achieved with the action of other living organisms, often called natural enemies [5,8]. The interaction of pests and their natural enemies (organisms that can cause injury to the pests, such as predators and parasites) can be represented by predator-prey models.…”