An Improved Particle Swarm Optimization Algorithm Based on Two Sub-swarms
In order to improve performance of particle swarm optimization algorithm (PSO) in global optimization, the reason of premature convergence of the PSO is analyzed, and a new particle swarm optimization based on two sub-swarms (TSS-PSO) is proposed in this paper. The particle swarm is divided into two identical sub-swarms, that is, the first sub-swarm adopts basic PSO model to evolve, whereas the second sub-swarm iterates adopts the cognition only model. In order to enhance the diversity and improve the convergence of the PSO, the worst fitness of the first sub-swarm is exchanged with the best fitness of the second sub-swarm in each iterate for increasing the information exchange between the particles. Compared with other two sub-swarms algorithms, the idea of this algorithm is readily comprehended, and its program is easy to be realized. The experimental results display that the convergence of TSS-PSO evidently gets the advantage of basic particle swarm optimization, as well as its competence of finding the global optimal solution is better than the basic PSO.
KeywordsParticle Swarm Optimization Precocity Converges Global Optimization Sub-Swarm
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