Research on Chaos-Improved Ant Colony Algorithm and Its Application in PID Control for Marine Boiler Pressure

This research aims to investigate the chaos-improved ant colony algorithm and explore its application in PID control for marine boiler pressure systems. The ant colony algorithm is a heuristic optimization method that simulates ant foraging behavior to solve complex optimization problems. In our implementation, we enhance the traditional algorithm by incorporating chaotic elements through logistic map functions, which improves global search capability and convergence speed by maintaining population diversity during iteration cycles.

Marine boiler pressure control represents a critical challenge in marine engineering. Effective pressure regulation ensures operational safety and navigation efficiency. Conventional PID control methods have been widely adopted in marine boiler systems. However, due to the inherent complexity and nonlinear characteristics of boiler dynamics, traditional PID controllers may exhibit suboptimal performance under certain operational conditions, particularly during transient states or load variations.

This study investigates the feasibility of integrating the chaos-improved ant colony algorithm with PID controllers for marine boiler pressure regulation. The algorithm implementation involves optimizing PID parameters (Kp, Ki, Kd) through pheromone update mechanisms and chaotic perturbation operations. Key functions include chaotic initialization of ant positions, adaptive pheromone evaporation rates, and chaotic local search operations to prevent premature convergence. The integration enhances control system robustness and stability by combining the exploration capabilities of chaotic systems with the exploitation strengths of ant colony optimization.

In conclusion, this research conducts an in-depth investigation into chaos-improved ant colony algorithms and their practical implementation in marine boiler pressure PID control systems. The study aims to provide innovative solutions and methodological frameworks for addressing control challenges in marine engineering applications, with potential extensions to other industrial control systems requiring adaptive optimization techniques.