Research on the Application of Ant Colony Optimization in Continuous Space Optimization Problems

Ant Colony Optimization (ACO) is a metaheuristic algorithm inspired by the foraging behavior of ants, widely applied in solving continuous space optimization problems. The algorithm simulates the pheromone deposition and evaporation processes during ants' food search to locate optimal solutions. In implementation, artificial ants construct solutions probabilistically based on pheromone trails and heuristic information, typically using Gaussian kernel functions for continuous domain sampling. Key algorithmic components include: - Solution construction via probability density functions for continuous variables - Pheromone update mechanisms with evaporation rate control - Adaptive parameter tuning (e.g., α, β, ρ parameters) for exploration-exploitation balance The algorithm efficiently navigates complex continuous search spaces and optimizes multiple objective functions through iterative refinement. Research areas encompass algorithmic principles, parameter adjustment strategies (e.g., using convergence detection for dynamic parameter adaptation), and performance evaluation metrics (like solution quality and convergence speed). ACO provides an effective methodology for continuous optimization challenges, with practical implementations often involving: - Boundary handling techniques for constrained optimization - Hybrid approaches combining with local search methods - Parallelization strategies for large-scale problems