Visualizing particle swarm optimization - Gaussian particle swarm optimization

Particle swarm optimization (PSO) conjures an image of particles searching for the optima the way bees buzz around flowers. One approach at visualizing the swarm graphs where all the particles are each generation, thus demonstrating the random nature associated with swarms of insects. Another approach is to show successive bests, thus showing the way that the swarm progresses. Some have even looked at the specific search path of the particle that eventually finds the optima. These approaches provide limited understanding of PSO. This paper presents a new visualization approach based on the probability distribution of the swarm, thus the random nature of PSO is properly visualized. The visualization allows better understanding of how to tune the algorithm and depicts weaknesses. A new algorithm based on moving the swarm a Gaussian distance from the global and local best is presented. Gaussian particle swarm optimization (GPSO) is compared to PSO.