A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer

In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.

Automated summary

In this paper, a novel PSO algorithm is proposed with a sigmoid-function-based weighting strategy to adaptively adjust acceleration coefficients, enhancing the convergence rate by considering distances to global and personal best positions. The algorithm, inspired by neural networks, outperforms some popular PSO algorithms in improving the convergence rate according to comprehensive evaluation on benchmark functions.