Ranking-based biased learning swarm optimizer for large-scale optimization
作者： Hanbo Deng , Lizhi Peng , Haibo Zhang , Bo Yang , Zhenxiang Chen
DOI: 10.1016/J.INS.2019.04.037
关键词:
摘要: Abstract Large-scale optimization, solving real high-dimensional problems, has attracted many research interests. optimization problems are far more difficult than traditional due to their numerous local optimum. In this paper, a principle of maximizing the fitness difference between learners and exemplars is proposed improve performance algorithm. Then based on principle, improved particle swarm algorithm called “ranking-based biased learning optimizer for large-scale optimization” (RBLSO) proposed. The RBLSO contains two types strategies, namely, ranking paired (RPL) center (BCL). RPL, worse particles learn peer from better according ranks, so then convergence speed will be accelerated. BCL, each learns that defined as weighted whole swarm. This operator utilized strengthen explorative ability To test performances algorithm, we conduct some experiments mechanism. compared with several state-of-the-art algorithms widely used benchmark function sets, CEC2010 CEC2013. These sets were special session competition global held under Congress Evolutionary Computation (CEC) 2010 2013. Experimental results show effective in problems.
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