Enhancing Particle Swarm Optimization with Socio-cognitive Inspirations

Following recently published socio-cognitively inspired ACO concept for global optimization, we try to verify the proposed idea by adapting the PSO in a similar way. The swarm is divided into species and the particles get inspired not only by the global and local optima, but share the knowledge about the optima with neighbourhood agents belonging to other species. After presenting the concept and motivation, the experimental results gathered for common benchmark functions tackled in 100 dimensions are shown and the efficacy of the proposed algorithm is discussed.