1. Ant Colony Optimisation: Applications
TEAM B Alyssa Hondrade Malin Rosenburg Ryan Bunney Shashank Rai

2. Contents Recap of Ant Colony Optimisation (ACO) Applications
What is ACO?
Requirements
Applications
Generalised Travelling Salesman
Feature Selection
Advantages of ACO
Success of ACO in the real world
Outlooks and Conclusions

3. What is Ant Colony Optimisation?
Algorithm introduced in the 1990s
Solving combinatorial optimisation problems
Metaheuristic algorithm
Stochastic optimisation techniques
Biologically-influenced agents
Pheromones laid on used path
Pheromone decays over time
Toksari, M. D. (2016)

4. What is Ant Colony Optimisation?
Ants select direction probabilistically
Stopping criterion
Can be applied to any optimisation problem…
Dorigo et al. (2006)
Dorigo et al. (2006)

5. Requirements Graph representation Heuristic desirability of links
Positive feedback process
Constraint-satisfaction method
Solution construction method

6. Application 1: Generalised Travelling Salesman
Travelling salesman problem… …with a twist!
xkcd (2008)

7. Application 1: Problem Definition
Set of nodes that we need to traverse
Tour all nodes once, return to the original node
In the generalised TSP, nodes are m-sized clusters
Representative of suburbs, warehouses, etc.
We want to minimise the distance covered
Classic combinatorial-optimisation problem
Xypron (2010)

8. Application 1: Applying ACO
Dorigo et al. inspired by ants for application to the TSP
We have n number of nodes
These are grouped into clusters
We try and minimise the distance to one node in the cluster
Also need to keep track of visited/unvisited groups
Have a table of visited cities
Have a table of unvisited groups
The probability is calculated according to this value
Diaz, D. M. (2010)

9. Application 1: Applying ACO
We can add optimisations
“Group Influence”
Preference groups that have cities closer to the city we are in currently
Include this as an influence factor when calculating probability

10. Application 1: Results of ACO
Meta-heuristic that provide good performance and results under many different conditions
Easily transferred to the GTSP problem by adding the ‘group’ set parameter
Group Factor improves results for GTSP

11. Application 2: Feature Selection
“We are drowning in information but starved for knowledge.” - John Naisbitt
Definition: A search process or technique in data mining that selects a subset of salient features for building robust learning models, such as neural networks and decision trees. Feature selection is an important preprocessing technique in data prepreocessing for data mining.

12. Application 2: Problem Definition
High dimensionality of feature space, so need a way to:
Reduce the dimensionality
Improve efficiency and precision of the classifier
Classifier: In data mining, it is a function that assigns items in a collection to target categories or classes.
Why use a stochastic approach?
Why use ACO?

13. Application 2: Applying ACO
Requirements
Positive feedback
Graph representation
Termination condition:
Number of iterations
Heuristic desirability
Feature subset size
Solution construction method

14. Application 2: Applying ACO
Step 1: Initialisation
Step 2: Solution generation and evaluation of ants
Evaluation of the selected subsets
Check stop criterion
Pheromone updating
Generation of new ants
Aghdam et al. (2009)

15. Application 2: Results of ACO
Simple concept: primitive mathematical operators.
Interaction in the colony enhances, rather than detract from the progress to the solution.
An ant has memory.
Aghdam et al. (2009)

16. Advantages of ACO Dynamic problems Stochastic optimisation problems
Multiple objective
Parallel approach
Continuous optimisation

17. Success of ACO in the real world
Euro-Bios have applied ACO to a number of different scheduling problems such as a continuous two-stage flow shop problem with finite reservoirs.
Ant route, for the routing of hundreds of vehicles of companies such as:
Migros, the Swiss supermarket chain
Barilla, the Italian pasta maker.

18. Success of ACO in the real world
Ant Optima has devised an algorithm for vehicle routing problems.
The problems modelled includes various real world constraints, such as:
Set up times
Capacity restrictions
Resource compatibilities
Maintenance calendars
DYVOIL, for the management and optimisation of heating oil distribution with a non-homogenous fleet of trucks, used for the first time by Pina Petroli in Switzerland.

19. Outlooks and Conclusions
As we have discussed, nowadays hundreds of researchers worldwide are applying ACO to classic N P-hard optimisation problems, while only a few works concern variations that include dynamic and stochastic aspects as well as multiple objectives.
The study of how best to apply ACO to such variations will certainly be one of the major research directions in the near feature.
Crazy idea ACO… 15 years ago.

20. References
Aghdam, M.H., Ghasem-Aghaee, N. and Basiri, M.E., Text feature selection using ant colony optimization. Expert systems with applications, 36(3), pp
Blum, C., Ant colony optimization: Introduction and recent trends. Physics of Life reviews, 2(4), pp
Dorigo, M., Birattari, M. and Stutzle, T., Ant colony optimization. IEEE computational intelligence magazine, 1(4), pp
Prakasam, A. and Savarimuthu, N., Metaheuristic algorithms and probabilistic behaviour: a comprehensive analysis of Ant Colony Optimization and its variants. Artificial Intelligence Review, 45(1), pp
Prakasam, A. and Savarimuthu, N., Metaheuristic algorithms and polynomial turing reductions: a case study based on ant colony optimization. Procedia Computer Science, 46, pp
Yang, J., Shi, X., Marchese, M. and Liang, Y., An ant colony optimization method for generalized TSP problem. Progress in Natural Science, 18(11), pp