🐜 ACO & Emergent Systems
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# 🐜 ACO & Emergent Systems ## 1. 🎯 Learning Objectives - Explain emergent behavior in swarm intelligence - Trace ACO pheromone update for TSP - Compare ACO with genetic algorithms ## 2.
🐜 ACO & Emergent Systems
1. 🎯 Learning Objectives
- Explain emergent behavior in swarm intelligence
- Trace ACO pheromone update for TSP
- Compare ACO with genetic algorithms
2. 📖 Core Content
3.1 Emergent Behavior
Simple local rules → complex global behavior. Ants following pheromone trails find shortest paths without central coordination.
3.2 ACO Algorithm Details
Pheromone update: τᵢⱼ ← (1-ρ)τᵢⱼ + Σ Δτᵢⱼᵏ where Δτᵢⱼᵏ = Q/Lₖ if ant k used edge (i,j). Probability: Pᵢⱼ = τᵢⱼᵃ · ηᵢⱼᵝ / Σ τᵢₖᵃ · ηᵢₖᵝ
3.3 Parameters
| Parameter | Effect | Typical Value |
|---|---|---|
| α (pheromone weight) | Higher = more exploitation | 1 |
| β (heuristic weight) | Higher = more greedy | 2-5 |
| ρ (evaporation) | Higher = faster forgetting | 0.1-0.5 |
| Q (pheromone constant) | Scales deposit | 1 |