This is Ant_Colony_Optimization_PPT.pptx

Ant colony optimization

1. Ant Colony Optimization (ACO) Nature-Inspired Metaheuristic Algorithm Your Name | Course Info

2. Introduction • Ant Colony Optimization (ACO) is a nature-inspired algorithm • Inspired by real ants’ foraging behavior • Belongs to Swarm Intelligence and Metaheuristics

3. Biological Inspiration • Ants lay down pheromone trails • Shorter paths reinforced by more ants • Collective behavior leads to efficient path finding

4. History • Introduced by Marco Dorigo (1992) in his PhD thesis • Extended into multiple variants (AS, ACS, MAX-MIN ACO, etc.)

5. ACO Algorithm Steps 1. Initialize pheromone levels 2. Ants construct solutions 3. Update pheromone (evaporation + reinforcement) 4. Repeat until convergence

6. Probability Formula Pij^k = (τij^α * ηij^β) / Σ (τil^α * ηil^β) τ = pheromone intensity η = heuristic info (1/distance) α, β = control parameters

7. Applications • Traveling Salesman Problem (TSP) • Vehicle Routing Problems • Job Scheduling • Network Routing • Robotics & Path Planning • Machine Learning tasks

8. Advantages • Robust and flexible • Distributed (no central control) • Adaptable to dynamic problems

9. Limitations • Slow convergence for large problems • Risk of stagnation (early convergence) • Sensitive to parameter tuning

10. Variants of ACO • Ant System (AS) • Ant Colony System (ACS) • MAX-MIN ACO • Hybrid ACO (GA, PSO, etc.)

11. Case Study Example • Example: Solving TSP with ACO • Cities = nodes, distances = edges • Pheromone intensifies on shortest path • Illustrates adaptive optimization

12. Conclusion • ACO = powerful nature-inspired heuristic • Effective for combinatorial optimization problems • Future scope: Hybrid & Parallel ACO approaches

13. References • Dorigo, M. (1992). Optimization, Learning and Natural Algorithms. PhD Thesis. • Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. MIT Press.