A discrete particle swarm optimization(DPSO) algorithm based on multi-scale cooperative clone mutation
(MSCMDPSO) is proposed. The clone mutation operator with multi-scale possibilities is introduced on the current optimical
solution, which can not only improve the ability of local search, but also keep the abilities of global space 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 operators according to the
variation of the fitness value and makes mutation operators with smaller-scale possibilities implement local accurate minima
solution search at the late evolution. The experiment studies on 5 standard benchmark functions, and the experimental results
show the proposed method can not only effectively solve problem of lack of local search ability, but also significantly speed
up the convergence and improve the stability.