Robust Particle Swarm Optimizer based on Chemomimicry
This is an incremental improvement for optimization problems, particularly those involving experimental precision.
The authors tackled the problem of local minima in multimodal optimization by introducing a particle swarm optimizer based on chemomimicry with a chaos factor, resulting in improved robustness compared to standard PSO.
A particle swarm optimizer (PSO) loosely based on the phenomena of crystallization and a chaos factor which follows the complimentary error function is described. The method features three phases: diffusion, directed motion, and nucleation. During the diffusion phase random walk is the only contributor to particle motion. As the algorithm progresses the contribution from chaos decreases and movement toward global best locations is pursued until convergence has occurred. The algorithm was found to be more robust to local minima in multimodal test functions than a standard PSO algorithm and is designed for problems which feature experimental precision.