Multi-swarm based optimization algorithm in dynamic environments
Lingli Yu · Journal of Central South University(Science and Technology) · 2009
A diversity measure was proposed for multi-swarm particle swarm optimization algorithms in dynamic environments. In order to improve the diversity of the swarm, each particle flew randomly away from its best neighbor particle before updating its velocity and location, and the redundant particles in each sub-swarm were replaced with random particles in search space. Subsequently, the change was detected by re-evaluating the objective function at the memorized best location of each particle. The results show that the diversification method can improve the diversity of sub-swarms with 60% more than that of a representative algorithm, and the proposed algorithm can efficiently track changing global optimum in high dynamic environments.