Yesterday I presented my work Migration Study on a Pareto-based Island Model for MOACOs, accepted as full-paper at the Genetic and Evolutionary Computation Conference 2013, held in Amsterdam.
The paper abstract is:
Pareto-based island model is a multi-colony distribution scheme recently presented for the resolution, by means of ant colony optimization algorithms, of bi-criteria problems. It yielded very promising results, but the model was implemented considering a unique Pareto-front-shaped unidirectional neighborhood migration topology, and a constant migration rate. In the present work two additional neighborhood topology schemes, and four different migration rates have been tested, considering the algorithm which obtained the best results in average in the model presentation article: MOACS (Multi-Objective Ant Colony System). Several experiments have been conducted, including statistical tests for better support the study. High values for the migration rate and the use of a bidirectional neighborhood migration topology yields the best results.
The last Wednesday (13 of May), we presented (again) our Multiobjective Ant Colony Optimization algorithm (yes, the famous CHAC :D) at NICSO 2010, which was held in Granada, in the same building where we work everyday…
… what a so far trip… :-| :D
The paper presents a study of the objective balancing parameter (named LAMBDA), used in this algorithm. ;)
Next week, Carlos Fernandes will present in the ANTS 2008 conference our paper KANTS: Artificial Ant System for Classification (hope the typo is not in the proceedings, but I’m afraid it will be). The algorithm was already presented by Antonio in ALIFE XI, with the paper KohonAnts: a self-organizing ant algorithm for clustering and pattern classification (which is also available from arxiv). Antonio was questioned about what was good about this algorithm, and I guess this is as good a place as any other to tell about it.
The basic idea of Kohonants is to use stigmergy for clustering and classification. Usual ant clustering algorithm place data as objects in the grid ants move around, and then, via some natural inspiration and a great deal of heuristics, they manage to cluster them according to proximity.
Kohonants, on the other hand, makes each data item an ant (or several, if needed). Pheromones are also vectorial in nature, in the same dimension as data, and what ants do when they move about is first take into account what’s the pheromone levels they have around in their neighborhood, and second modify it making them closer to the vector they represent.
That is why they are called Kohonen’s Ants: Kohonen’s algorithm behaves in the same way. Takes a training data vector, compares it to all the vectors in a two-dimensional array, and whoever wins is made closer to the data vector. Ants in Kohonants take the place of data vector in Kohonen’s algorithm, and the two-dimensional vector array that is trained is substituted by the two-dimensional (vectorial) pheromone field in Kohonants.
Results so far have been quite good, but we’ll continue with it to see what are their limits, and how well it fares against other ant and non-ant clustering algorithms. Meanwhile, as we mentioned in our previous post, you can download full code from the GeNeura code repository
Algorithms for decision support in the battlefield have to take into account separately all factors with an impact of success: speed, visibility, and consumption of material and human resources. It is usual to combine several objectives, since military commanders give more importance to some factors than others, but it is interesting to also explore and optimize all objectives at the same time, to have a wider range of possible solutions to choose from, and explore more efficiently the space of all possible paths. In this paper we introduce hCHAC-4, the four-objective version of the hCHAC bi-objective ant colony optimization algorithm, and compare results obtained with them and also with some other approaches (extreme and mono-objective ones). It is concluded that this new version of the algorithm is more robust, and covers more efficiently the Pareto front of all possible solutions, so it can be consider as a better tool for military decision support.
In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen’s Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.
We’ll be very soon doing the rounds with this algorithm with the usual conferences. It is new, it works surprisingly well, and, as usual, source code is available from your friendly Forja subversion repository (not updated, though).