Guo, Zhenxiong; Chen, Yuye; Xiao, Ke; He, Junjie; Liu, Chang; Pan, Shuwan; Chen, Songyan A dynamic perception coefficients self-tuning particle swarm optimization algorithm based on nonlinear systems. (Chinese. English summary) Zbl 1399.68196 J. Xiamen Univ., Nat. Sci. 56, No. 5, 704-710 (2017). Summary: We propose a dynamic perception-coefficients algorithm, called self-tuning particle swarm optimization (SPSO) in this paper, which is used for determining optimal nonlinear systems. The theory of SPSO integrates conventional PSO (CPSO) with neural network (NN) and trains perception coefficients with the gradient descent algorithm to improve computational efficiency, rate of convergence and global convergence. Then we combine proportional-integral-derivative (PID) NN with SPSO to optimize complex nonlinear systems. This paper presents four examples and another two optimization algorithms CPSO modified PSO (MPSO) to help estimate performances of the proposed SPSO. The result demonstrates that the SPSO exhibits great performances in global convergence, rate of convergence and strong robustness while optimizing complex nonlinear systems. MSC: 68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) Keywords:optimization algorithm; self-tuning particle swarm optimization; dynamic perception coefficients; neural network; self-tuning nonlinear system PDFBibTeX XMLCite \textit{Z. Guo} et al., J. Xiamen Univ., Nat. Sci. 56, No. 5, 704--710 (2017; Zbl 1399.68196) Full Text: DOI