Tesis doctoral de Aleksandar Jevtic
Animal swarms in nature are able to adapt to dynamic changes in their environment, and through cooperation they can solve problems that are crucial for their survival. Only by means of local interactions with other members of the swarm and with the environment, they can achieve a common goal more efficiently than it would be done by a single individual. This problem-solving behavior that results from the multiplicity of such interactions is referred to as swarm intelligence. The mathematical models of swarming behavior in nature were initially proposed to solve optimization problems. Nevertheless, this decentralized approach can be a valuable tool for a variety of applications, where emerging global patterns represent a solution to the task at hand. Methods for the solution of difficult computational problems based on swarm intelligence have been experimentally demonstrated and reported in the literature. However, a general framework that would facilitate their design does not exist yet. in this dissertation, a new general design methodology for swarm intelligence tools is proposed. By defining a discrete space in which the members of the swarm can move, and by modifying the rules of local interactions and setting the adequate objective function for solutions evaluation, the proposed methodology is tested in various domains. The dissertation presents a set of case studies, and focuses on two general approaches. One approach is to apply swarm intelligence as a tool for optimization and feature extraction, and the other approach is to model multi-agent systems such that they resemble swarms of animals in nature providing them with the ability to autonomously perform a task at hand. artificial swarms are designed to be autonomous, scalable, robust, and adaptive to the changes in their environment. In this work, the methods that exploit one or more of these features are presented. First, the proposed methodology is validated in a real-world scenario seen as a combinatorial optimization problem. Then a set of novel tools for feature extraction, more precisely the adaptive edge detection and the broken-edge linking in digital images is proposed. A novel data clustering algorithm is also proposed and applied to image segmentation. Finally, a scalable algorithm based on the proposed methodology is developed for distributed task allocation in multi-agent systems, and applied to a swarm of robots. The newly proposed general methodology provides a guideline for future developers of the swarm intelligence tools.
Datos académicos de la tesis doctoral «Swarm intelligence: novel tools for optimization, feature extraction, and multi-agent system modeling«
- Título de la tesis: Swarm intelligence: novel tools for optimization, feature extraction, and multi-agent system modeling
- Autor: Aleksandar Jevtic
- Universidad: Politécnica de Madrid
- Fecha de lectura de la tesis: 13/05/2011
Dirección y tribunal
- Director de la tesis
- Diego Andina De La Fuente
- Tribunal
- Presidente del tribunal: Juan isidoro Seijas martínez-echevarría
- marco Castellani (vocal)
- michael Packianather (vocal)
- Manuel Marqués crisóstomo (vocal)