A Triple Network Knowledge Learning Framework for Particle Swarm Optimization

Evolutionary computing (EC), as a metaheuristic algorithm, is widely applied in optimization problems due to its effectiveness. During the optimization process of EC, a large amount of data will be generated, which contains rich information on the evolutionary process. Mining and utilizing these data can learn promising evolutionary knowledge to assist the EC algorithms in achieving more efficient evolution. First, we analyze in detail the key differences in collecting experience and guiding evolution between particle swarm optimization (PSO) and differential evolution (DE) in the existing knowledge learning framework. Then, we propose a triple network knowledge learning (TNKL) framework to mitigate the limitations and boost knowledge learning for PSO-based algorithms. In the TNKL framework, the successful evolution experience of the current particle, the historical best position, and the global best position are collected. Three neural networks learn different knowledge from these experiences and give appropriate evolution directions. The TNKL framework then comprehensively considers these directions according to the evolution stage to guide particle evolution. Finally, we integrate the TNKL framework with PSO and its state-of-the-art variants. The benchmark function experimental results verify the effectiveness and efficiency of the TNKL framework.