Enhancing the particle swarm optimization based on equilibrium of distribution

Particle swarm optimization is an optimization tool which is inspired by the birds swarm social behavior. To solve the premature problem of the basic particle swarm optimization (bPSO), a novel modified particle swarm optimization based on swarm particles equilibriums distribution (PSOED) was proposed. In order to effectively avoiding particles clustering within a sub-area of the problem scope, we proposed a new parameter which can measure the swarm particles equilibrium of distribution degree. It is introduced to guarantee the escaping from the sub-optimum trap. In addition, a new particle flying strategy is presented which can direct the particle rational flying behavior. Finally, four function optimizations are simulated to indicate that the PSOED have strong global search ability and are more efficient than the bPSO and GA.