Particle swarm optimization with damping factor and cooperative mechanism

A novel variant of particle swarm optimization with damping factor and cooperation mechanism (PSO-DFCM) to search the global optima in a large scale and high-dimensional searching space. In this optimal searching strategy, one balances the exploring and exploiting abilities of particles by introducing a new parameter, a damping factor α which is used to adjust the position information inherited from the previous state. The cooperative mechanism between the global-best-oriented and the local-best-oriented swarms is employed to help find the global optima quickly. In order to reduce the negative effect of unfavorable particles on swarm evolution, a new concept of evolution history, the least optimal particle in individuals’ histories — pleast, is defined to decide whether current information of particles is abandoned and reinitialized in our proposal. Also, fuzzy c-means clustering is applied to cluster the particles’ positions for the neighborhood establishment of individuals. Our comparative study on benchmark test functions demonstrates that the proposed PSO outperforms the standard PSO and three state-of-art variants of PSO in terms of global optimum convergence and final optimal results.