Particle Swarm Optimization with Triggered Mutation and Its Implementation based on GPU

摘要

A novel particle swarm optimization with triggered mutation (PSO-TM) is presented in this paper for better performance. First, a technique is designed to evaluate the "health" of swarm. When the swarm is successively "unhealthy" for a certain number of iterations, uniform mutation is applied to the position of each particle in a probabilistic way. If the mutations produce worse particles, the memorized previous positions are retrieved as current positions of these particles, hence the normal evolution process of the swarm will not be fiercely interrupted by such bad mutations. Experiments are conducted on 29 benchmark test functions to show the promising performance of our proposed PSO-TM. The results show that the PSO-TM performs much better than the standard PSO on almost all of the 29 test functions, especially those multimodal, complex ones of hybrid composition. Besides, PSO-TM adds little computation complexity to the standard PSO, and runs almost equally fast. Furthermore, we have implemented PSO-TM based on Graphic Processing Unit (GPU) in parallel. Compared with the CPU-based standard PSO, the proposed PSO-TM can reach a speedup of 25×, as well as an improved optimizing performance.