Ant Colony Optimization Algorithm for Shortest Path Finding

In computer science, Ant Colony Optimization (ACO) is a heuristic algorithm designed to find shortest paths in graphs. This algorithm simulates the pheromone trail-laying behavior of ants during food search activities, where multiple iterations help converge toward optimal solutions. The core implementation typically involves: initializing pheromone levels on graph edges, simulating ant movements through probabilistic path selection based on pheromone concentrations and heuristic information (like distance), and updating pheromone trails through evaporation and reinforcement mechanisms. Key functions include path construction using state transition rules and global/local pheromone update procedures. This algorithm proves highly effective in practical applications such as route planning, power network optimization, and bioinformatics. Additionally, as ACO operates as a distributed algorithm through parallel ant agents, it demonstrates significant scalability advantages when handling large-scale optimization problems.