查看文章

Large-scale optimization, solving real high-dimensional problems, has attracted many research interests. Large-scale optimization problems are far more difficult than traditional optimization problems due to their numerous local optimum. In this paper, a principle of maximizing the fitness difference between learners and exemplars is proposed to improve the performance of the optimization algorithm. Then based on the principle, a improved particle swarm optimization algorithm called the “ranking-based biased learning swarm optimizer for large-scale optimization” (RBLSO) is proposed. The proposed RBLSO contains two types of learning strategies, namely, ranking paired learning (RPL) and biased center learning (BCL). In RPL, the worse particles learn peer to peer from the better particles according to their ranks, so then the convergence speed will be accelerated. In BCL, each particle learns from the biased …