Particle Swarm Algorithm: Convergence and Applications
作者： Shichang Sun , Hongbo Liu
DOI: 10.1016/B978-0-12-405163-8.00006-5
关键词:
摘要: In this chapter, we present the convergence analysis and applications of particle swarm optimization algorithm. Although it is difficult to analyze algorithm, discuss its based on iterated function system probabilistic theory. The dynamic trajectory described single individual. We also attempt theoretically prove that algorithm converges with a probability 1 toward global optimal. apply algorithms solve scheduling problem peer-to-peer neighbor selection problem. This chapter concerned employ nature-inspired methods in machine learning. introduce reoptimize hidden Markov models.
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