Last weekend we were in Barcelona presenting two papers at the 4th International Conference on Evolutionary Computation Theory and Applications (ECTA 2012). One is an extension of the work presented at PPSN in the last September and its title is “Using Self-Organized Criticality for Adjusting a Particle Swarm” (C.M. Fernandes, J.J. Merelo and A.C. Rosa). We use the Bak-Sneppen model (which is known to display Self-Organized Critically) for controlling the parameters of the Particle Swarm Optimization (PSO) algorithm. We test the proposed strategy on two different topologies for the swarm and show that the performance is very stable throughout the test set. The paper and the corresponding talk won the best paper award of the congress. This is the abstract:
The local and global behavior of Self-Organized Criticality (SOC) systems may be an efficient source for controlling the parameters of a Particle Swarm Optimization (PSO) without hand-tuning. This paper proposes a strategy based on the SOC Bak-Sneppen model of co-evolution for adjusting the inertia weight and the acceleration coefficients values of the PSO. In order to increase exploration, the model is also used to perturb the position of the particles. The resulting algorithm is named Bak-Sneppen PSO (BS-PSO). An experimental setup compares the new algorithm with versions of the PSO with varying inertia weight, including a state-of-the-art algorithm with dynamic variation of the weight value and perturbation of the particles’ positions. The parameter values generated by the model are investigated in order to understand the dynamics of the algorithm and explain its performance.
And this is the presentation:
GeNeura Team 