Ant Colony Optimization for Shortest Path Problems
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In this document, we explore a fascinating and practical topic: Ant Colony Optimization (ACO). ACO is a biologically-inspired algorithm that simulates ant foraging behavior to find optimal paths, with applications spanning various domains including dynamic pathfinding. Here we present a universal MATLAB implementation for computing shortest paths using ACO principles. This program not only demonstrates the core algorithm mechanics but also provides practical solutions for real-world optimization problems. The implementation features pheromone matrix updates, probabilistic path selection using roulette wheel selection, and evaporation mechanisms to avoid local optima. Key functions include path cost calculation, pheromone level initialization, and iterative optimization loops with convergence criteria. Let's begin our exploration of the intriguing mechanics behind ant colony optimization!
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