Open AccessMarkov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer
Author(s) -
Chao-Wei Chou,
Jiann-Horng Lin,
Rong Jeng
Publication year - 2013
Publication title -
international journal of soft computing/international journal on soft computing
Language(s) - English
Resource type - Journals
eISSN - 2229-7103
pISSN - 2229-6735
DOI - 10.5121/ijsc.2013.4201
Subject(s) - computer science , markov chain , particle swarm optimization , selection (genetic algorithm) , mathematical optimization , artificial intelligence , algorithm , machine learning , mathematics
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochasticbehavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical inthe performance of PSO. As far as our investigation is concerned, most of the relevant researches arebased on computer simulations and few of them are based on theoretical approach. In this paper,theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in thispaper, which contains all the information needed for the future evolution. Then the memory-less property ofthe state defined in this paper is investigated and proved. Secondly, by using the concept of the state andsuitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markovchain is established. Finally, according to the property of a stationary Markov chain, an adaptive methodfor parameter selection is proposed