A Particle Swarm Optimization-Based Generative Adversarial Network

At present, the combination of general evolutionary algorithms (EAs) and neural networks is limited to optimizing the framework or hyper parameters of neural networks. To further extend applications of EAs on neural networks, we propose a particle swarm optimization (PSO) based generative adversarial network(GAN), named as PGAN in this paper. In the study, PSO is utilized as a generator to generate fake data, while the discriminator is a traditional fully connected neural network. In the confrontation process, when the proposed PSO can generate a better fake image, this will react to the discriminator, so that the discriminator can improve the recognition effect of the image and the better discriminator also accelerates the evolution of the overall model. Through experiments, we explore the new application value of EAs in deep learning, so that the sample data in EAs and the sample data in deep learning are interconnected. The PSO algorithm is improved, so that it truly participates in the confrontation with multi-layer perceptrons.