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

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

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)

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)

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

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

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)

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)

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

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

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.

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?

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

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)

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)

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

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.

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.

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.

References Aghdam, M.H., Ghasem-Aghaee, N. and Basiri, M.E., 2009. Text feature selection using ant colony optimization. Expert systems with applications, 36(3), pp.6843-6853. Blum, C., 2005. Ant colony optimization: Introduction and recent trends. Physics of Life reviews, 2(4), pp.353-373. Dorigo, M., Birattari, M. and Stutzle, T., 2006. Ant colony optimization. IEEE computational intelligence magazine, 1(4), pp.28-39. Prakasam, A. and Savarimuthu, N., 2016. Metaheuristic algorithms and probabilistic behaviour: a comprehensive analysis of Ant Colony Optimization and its variants. Artificial Intelligence Review, 45(1), pp.97-130. Prakasam, A. and Savarimuthu, N., 2015. Metaheuristic algorithms and polynomial turing reductions: a case study based on ant colony optimization. Procedia Computer Science, 46, pp.388-395. Yang, J., Shi, X., Marchese, M. and Liang, Y., 2008. An ant colony optimization method for generalized TSP problem. Progress in Natural Science, 18(11), pp.1417-1422.