@article {IOPORT.06090727,
    author       = {Lu, Feng and Gao, Liqun},
    title        = {A novel optimization mechanism based on an improved particle swarm optimization algorithm.},
    year         = {2011},
    journal      = {Journal of Northeastern University. Natural Science},
    volume       = {32},
    number       = {9},
    issn         = {1005-3026},
    pages        = {1221-1224},
    publisher    = {Editorial Department of Journal of Northeastern University, Nanhu, Shenyang},
    abstract     = {Summary: The aim of this paper is to accelerate searching speed and optimization accuracy of traditional PSO, an improved particle swarm optimization (PSO) algorithm is presented. Regularly varying function and slowly varying function are introduced in the position and velocity update formula. New mechanisms such as explorative operator and exploitative operator are formulated. At the beginning, most elements are updated by the explorative operator in the entire search space sufficiently. Within the iterations, more and more particles are handled by the exploitative operator, which are useful to overcome the deceptions of multiple local optima. It can be seen from the simulation results of the standard benchmark test functions that the proposed algorithm not only accelerates the convergence process, but also improves global optimization ability.},
    identifier   = {06090727},
}