Biogeography based optimization

Today, due to lack of volunteers, it was my turn again to deliver a paper seminar, and I chose Biogeography-Based Optimization, recently published in IEEE Transactions on Evolutionary Computation.
Interesting paper: a new nature-inspired algorithm from first principles. Conceptually, it is quite similar to estimation of distribution algorithms. The main concept is that of habitat, which have characteristics (represented by a vector) that make them suitable for a certain number of species; the better the HSI (habitat suitability index), the higher the number of species it can support. The population can migrate from one habitat to another, with flow going from those with more population to those with less population. Habitat characteristics can also change, in a way similar to mutation. Migration takes the place of crossover, with habitat variables migrating from those with a high population with those with a low population.
This more or less simple model yields rather good results, although it will probably be developed further in the future. It would be interesting, for instance, to add population structures, or to see how it could be paralellized.

New paper on multiobjective ant colony optimization available

Springer alerts me about the availability of the paper hCHAC-4, an ACO Algorithm for Solving the Four-Criteria Military Path-finding Problem, which was published some time ago at the NICSO 2007 conference. Here’s the abstract:

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.

If SpringerLink is not available at your institution and you are interested in a copy, please drop us a line. This article is further ahead the research line than the previous article, which used two objectives that were an aggregate of several sub-objectives. Results are better in this case, and all sub-objectives can be pursued at the same time.