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. ;)
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.