Multi-objective evolutionary optimization of biological pest control with impulsive dynamics in soybean crops

Area of Science:

• Agricultural Science
• Ecology
• Optimization Theory

Background:

• Biological pest control is an eco-friendly agricultural practice that manages pest populations below economic injury levels using natural enemies.
• Existing models often simplify the impulsive nature of pest control actions, which occur at discrete time intervals.

Purpose of the Study:

• To develop a multi-objective numerical solution for biological pest control in soybean crops.
• To optimize pest control by considering both application costs and economic damage costs.
• To model nonlinear systems with impulsive control dynamics for realistic application.

Main Methods:

• A nonlinear system model with impulsive control dynamics was developed.
• The dynamic optimization problem was solved using the NSGA-II (Nondominated Sorting Genetic Algorithm II), a multi-objective genetic algorithm.
• The model considers discrete time instants for control actions.

Main Results:

• The study proposes a dual pest control policy.
• The optimal strategy depends on the relative price of the control action versus the additional harvest yield.
• This leads to a choice between a low or a higher intensity control action strategy.

Conclusions:

• A flexible, cost-benefit-driven approach to biological pest control can be effectively implemented using multi-objective optimization.
• The NSGA-II algorithm provides a robust solution for complex, impulsive control dynamics in agricultural pest management.
• This research offers a practical framework for optimizing sustainable pest control strategies in soybean cultivation.