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Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behaviour

Author
Comellas, F.; Martinez Navarro, J.
Type of activity
Book chapter
Book
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
First page
811
Last page
814
Date of publication
2009-06-12
ISBN
978-1-60558-326-6
DOI
https://doi.org/10.1145/1543834.1543949
Repository
http://hdl.handle.net/2117/2594
URL
http://upcommons.upc.edu/e-prints/handle/2117/2594
Abstract
This paper introduces a multiagent optimization algorithm inspired by the collective behavior of social insects. In our method, each agent encodes a possible solution of the problem to solve, and evolves in a way similar to real life insects. We test the algorithm on a classical difficult problem, the $k$-coloring of a graph, and we compare its performance in relation to a standard genetic algorithm and another multiagent system. The results show that this algorithm is faster and outperforms ...
Keywords
Multiagent system Combinatorial optimization Graph coloring Adaptative complex systems
Group of research
COMBGRAPH - Combinatorics, Graph Theory and Applications