Ant Colony Optimization Algorithm Simulation

In this paper, we provide a comprehensive overview of Ant Colony Optimization (ACO) algorithm simulation. We explore the origins and evolution of ACO, and conduct an in-depth investigation into its applications across various problem domains, including the Traveling Salesman Problem (TSP), connectivity problems, and minimum spanning tree problems. The implementation process of ACO is detailed with emphasis on key algorithmic components such as pheromone update mechanisms, probability selection functions, and evaporation rates. We employ MATLAB for simulation, utilizing built-in functions for matrix operations and custom implementations for colony movement logic. Performance evaluation is conducted through comparative analysis of different parameter configurations and algorithm variations, examining convergence behavior and solution quality metrics. Finally, we discuss the advantages and limitations of ACO, including its robustness in handling combinatorial optimization problems and sensitivity to parameter tuning, along with potential future research directions involving hybrid approaches and parallel implementations.