@article {IOPORT.06041368,
    author       = {Tao, Xinmin and Xu, Jing and Wang, Yang and Liu, Yu},
    title        = {Discrete particle swarm optimization based on multi-scale cooperative clone mutation.},
    year         = {2011},
    journal      = {Control and Decision},
    volume       = {26},
    number       = {5},
    issn         = {1001-0920},
    pages        = {700-706},
    publisher    = {Editorial Office of Control and Decision, Northeastern University, Shenyang},
    abstract     = {Summary: A discrete particle swarm optimization (DPSO) algorithm based on multi-scale cooperative clone mutation (MSCMDPSO) is proposed. A clone mutation operator with multi-scale possibilities is introduced on the current optimal solution, which can not only improve the local search ability but also keep the abilities of global search and escaping from local optima. The mutation operator with large-scale possibilities can be utilized to quickly localize the global optimized space at the early evolution. The scale-changing strategy produces a smaller multi-scale mutation operator according to the variation of the fitness value and generates a mutation operator with smaller-scale possibilities implementing local accurate minima solutions search in the late evolution. Experimental studies on 5 standard benchmark functions and the obtained results show that the proposed method has not only the local search ability but also significantly speeds up the convergence and improves the stability.},
    identifier   = {06041368},
}