A novel particle swarm optimization algorithm based on particle migration

Inspired by the migratory behavior in the nature, a novel particle swarm optimization algorithm based on particle migration (MPSO) is proposed in this work. In this new algorithm, the population is randomly partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization with time varying inertia weight and acceleration coefficients (LPSO-TVAC). At periodic stage in the evolution, some particles migrate from one complex to another to enhance the diversity of the population and avoid premature convergence. It further improves the ability of exploration and exploitation. Simulations for benchmark test functions illustrate that the proposed algorithm possesses better ability to find the global optima than other variants and is an effective global optimization tool.