Optimizing Models Using Continuous Ant Algorithms

1. Optimizing Models Using Continuous Ant Algorithms Oleg Kovářík kovaro1@fel.cvut.cz Pavel Kordík kordik@fel.cvut.cz http://cig.felk.cvut.cz Computational Intelligence Group Department of Computer Science and Engineering Faculty of Electrical Engineering Czech Technical University in Prague

2. Content • GAME models • Continuous Ant Algorithms • Spiral data problem • Interesting results

3. GAME

4. Types of units

5. Optimization methods • QuasiNewton • SADE • PSO • Hybrid GA-PSO • Differential Evolution • (Stochastic) Orthogonal Search • Conjugate Gradient • Ant algorithms – AACA, ACO*, CACO, DACO • Random • Powell ...

6. GAME Select: transfer function, parameters, optimization method for each unit Genetic Algorithm

7. GAME Genetic Algorithm

8. GAME

9. Ant Colony Optimization (ACO)‏ Which next? Nearest? Used in best solutions? Pheromone map

10. Direct Application of ACO DACO (Min Kong, Peng Tian 2006)‏ n variables xi with normal distribution N (μi , σi), i ∈ {1, · · · , n} Updates by global best solution x: x2 x1 μ(t) = (1 − ρ) μ (t − 1) + ρx σ(t) = (1 − ρ) σ (t − 1) + ρ|x − μ(t − 1)|

11. Extended ACO ACO* (Socha 2004)‏ complex pheromone distribution Gaussian kernel PDF x1

12. Two spirals dataset Classification 2 classes

13. Training and testing set

14. DACO Uses sinus units to generate fractal-like solution

15. Results on testing dataset

16. Conclusion • ACO was able to utilize sinus units • It was useful to try different methods • We need to visualize the learning process

17. Thank you