Growing particle swarm optimizers with variable topology and dynamic parameter

This paper studies a new version of growing particle swarm optimizers. In the algorithm, a new particle is born if a particle exploring the optimum is stagnated and the swarm can grow in variable tree or multi-loop topology depending on problem complexity. The particle velocity is controlled by a dynamic acceleration parameter that can attenuate depending on the number of particles and can vibrate depending on the time. The growing structure and the dynamic parameter play important role to reduce the computation cost and to increase the success rate. The algorithm efficiency is confirmed by numerical experiments for typical benchmarks.