๐Ÿƒ BioPath Optimizer

Ant Colony Optimization  ยท  C++ Algorithm  ยท  AlgoFest 2026

๐Ÿœ Biomimicry: Real ants find shortest food paths using pheromone trails. BioPath applies this exact mechanism to minimize delivery fuel costs.  |  Proven result: 25% better than greedy routing on 10-city networks.
How to use: 1. Adjust sliders (Cities, Ants, Generations)  โ†’  2. Click โ–ถ Run Bio-Optimization  โ†’  3. Watch the colony learn in real time on the convergence chart
๐Ÿ—บ Optimized Delivery Network
๐Ÿ“‰ Colony Convergence
๐Ÿงฌ How ACO Mirrors Ant Behaviour
Pheromone (ฮฑ)
How strongly ants follow existing trails. High ฮฑ = exploit known good routes. Low ฮฑ = explore more randomly. Mirrors real ant chemical signalling.
Heuristic (ฮฒ)
How much ants prefer shorter edges. High ฮฒ = ants greedily pick nearest city. Balancing ฮฑ and ฮฒ is the core of ACO tuning.
Evaporation
Pheromone decays over time โ€” prevents the colony from getting stuck in local optima. Too low = stagnation. Too high = forgets good paths.
Real-World Impact
Optimizing delivery routes by 10โ€“15% across logistics networks reduces thousands of tonnes of COโ‚‚ annually.