Towards Better Coordination of Rescue Teams in Crisis Situations: A Promising ACO Algorithm

International audienceCrisis management challenges decision support systems designers. One problem in the decision marking is developing systems able to help the coordination of the different involved teams. Another challenge is to make the system work with a degraded communication infrastructure. Each workstation or embedded application must be designed such as potential decisions made trought other workstations are treated as eventualities. We propose in this article a multi-agent model, based on an ant colony optimization algorithm, and designed to manage the inherent complexity in the deployment of resources used to solve a crisis. This model manages data uncertainty. Its global goal is to optimize in a stable way fitness functions, like saving lives. Moreover, thanks to a reflexive process, the model manages the effects of its decisions into the environment to take more appropriate decisions. Thanks to our transactional model, the system takes into account a large data amount and finds global optimums without exploring all potential solutions. In perspective, users will have to define rules database thanks to an adapted graphical interface. %Each rule associates, for each potential event, a goal with its fitness functions, and a list of possible tasks to do. Then, if the nature of the crisis is deeply unchanged, users should be able to change rules' databases

An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP

Abstract. The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the Ant Colony Optimization metaheuristic. In this contribution, the existing algorithms of this kind are reviewed and experimentally tested in several instances of the bi-objective traveling salesman problem, comparing their performance with that of two well-known multi-objective genetic algorithms.

A New Approach for Making Use of Negative Learning in Ant Colony Optimization

The overwhelming majority of ant colony optimization approaches from the literature is exclusively based on learning from positive examples. Natural examples from biology, however, indicate the potential usefulness of negative learning. Several research works have explored this topic over the last two decades in the context of ant colony optimization, with limited success. In this work we present an alternative proposal for the incorporation of negative learning in ant colony optimization. The results obtained for the capacitated minimum dominating set problem indicate that this approach can be quite useful. More specifically, our extended ant colony algorithm clearly outperforms the standard approach. Moreover, we were able to improve the current state-of-the-art results in 10 out of 36 cases

Control Theoretical Challenges in Systems Biology

In M. Dorigo, L. Gambardella, F. Mondada, T. St¨utzle, M. Birratari, and C. Blum, editors, ANTS’2004, Fourth International Workshop on Ant Algorithms and Swarm Intelligence, Springer Verlag, Berlin, Germany.info:eu-repo/semantics/publishe