FDC-BASED PARTICLE SWARM OPTIMIZATION
Antonia Azzini, Stefano Cagnoni, Leonardo Vanneschi · 2009
A new variant of the Particle Swarm Optimization (PSO) algorithm is presented in this paper. It uses a well-known measure of problem hardness, the Fitness-Distance Correlation, to modify the position of the swarm attractors, both global and local to single particles. The goal of the algorithm is to make the fitness landscape between each particle's positions and their attractors as smooth as possible. Experimental results, obtained on 15 out of the 25 test functions belonging to the test suite used in CEC-2005 numerical optimization competition, show that this new PSO version is generally competitive, and in some cases, better than standard PSO.