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Content archived on 2024-06-12

Towards a framework for ant colony optimization


Research objectives and content
Ant Colony Optimization (ACO) is a new approach to the solution of hard combinatorial optimization problems. ACO can be characterized as a population oriented, adaptive search procedure that uses positive feedback to guide search. The research project will address the following three key issues:
1. Study of all previously proposed ACO algorithms and design of a unified framework. 2. Detailed comparison of the new framework with other general approaches like Simulated Annealing, Genetic Algorithms, and Tabu Search. 3. Study of the application of ACO to scheduling problems. The benefits of the research are the following. By a unified framework the classification and improvement of the existing approaches to ACO will be easier, a comparison of ACO to other approaches will identify its particular strengths and the application of ACO to scheduling problems will give further insights into the class of problems that can be successfully attacked by ACO.
Training content (objective, benefit and expected impact)
By the research project I will sharpen my understanding of ACO, gain deeper insights into other solution approaches, and constrained optimization problems.
Links with industry / industrial relevance (22)
IRIDIA is involved in many industrial research projects. A direct link is given by an industrial project funded by MARS - Master Food - that is carried out currently at IRIDIA.

Call for proposal

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Université Libre de Bruxelles
EU contribution
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87,Avenue A. Buyl
1050 Bruxelles

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Total cost
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