Abstract
In this article, a newmodel for particle swarm optimization (PSO) is proposed. In this model, each particle’s
behaviour is influenced by the best experience among its neighbours, its own best experience and all its
components. The influence among different components of particles is implemented by the online training
of a multi-input single-output back propagation (BP) neural network. The inputs and outputs of the BP
neural network are the particle position and its tendency to the best position, respectively. Therefore, the
new structured PSO model is called a fully connected particle swarm optimizer (FCPSO). Simulation
results and comparisons with exiting PSOs demonstrate that the proposed FCPSO effectively enhances the
search efficiency and improves the search quality. Engineering Optimization