<ul data-eligibleForWebStory="false"><li>Ant Colony Optimization (ACO) models mimic how ants collectively discover optimal paths, a process decentralized, adaptive, and efficient.</li><li>The article delves into applying ACO to solve the NP-hard Travelling Salesman Problem (TSP) through a JavaScript-based CLI AI system.</li><li>ACO involves ants exploring paths randomly, laying pheromones on paths, biasing probabilities toward better paths over time, employing positive feedback, distributed computation, and stigmergy for efficient solutions.</li><li>The ACO-TSP implementation includes features like graph modeling, swarm simulation, pheromone updates, multi-agent memory competition, and real-time visual debugging, demonstrating improvements in problem-solving and offering broader AI implications.</li></ul>

How Ants Solve the Hardest Problems: A Deep Dive into ACO and the Travelling Salesman

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