id: 06002379
dt: j
an: 06002379
au: Liu, Zhaohua; Zhang, Yingjie; Zhang, Jing; Wu, Jianhui
ti: A novel binary-state immune particle swarm optimization algorithm.
so: Control Theory Appl. 28, No. 1, 65-72 (2011).
py: 2011
pu: South China University of Technology, Guangzhou
la: ZH
cc: 
ut: particle swarm optimization algorithm; elitist learning; artificial immune
    system; multimodal function optimization
ci: 
li: 
ab: Summary: Conventional particle swarm optimization (PSO) algorithms become
    often trapped in local optima when performing a global optimization. A
    novel binary-state immune particle swarm optimization algorithm (BIPSO)
    is proposed. In order to enhance the exploration capacity of the
    algorithm while avoiding premature stagnation, the meta-heuristics
    allows for two states of behavior of the particles, a gather state and
    an explore state, during the search. During the iterations, the
    population is divided into two parts. The elitist learning strategy is
    applied to the elitist particles in order to help them to jump out of
    local optimal regions when the search identifies them to be in the
    gather state. An exploration strategy is proposed to encourage
    particles in the explore state to escape from the local region.
    Moreover, in order to increase the diversity of the population and to
    improve the search capabilities of the PSO algorithm, clone selection
    and receptor editing are introduced. Experiments on several benchmarks
    show that the proposed method is capable of improving the search
    performance. It is efficient in tackling high dimensional multimodal
    optimization problems.
rv: