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

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 kcoloring 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 the other methods for a range of random graphs with different orders and densities. Moreover, the method is easy to adapt to solve different NP-complete problems. Categories and Subject Descriptors: I.2.11 [Distributed

• Type: Journal
• Year: 2009
• Where: GECCO
• Authors: Francesc Comellas, Jesus Martinez-Navarro
• Keywords: Algorithm, GECCO 2009, Multiagent Optimization Algorithm, Optimization, Standard Genetic Algorithm