Constructing a Markov Chain on Particle Swarm Optimizer

The Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. As far as our investigation, most of the relevant researches are based on computer simulations and seldom of them are based on theoretical approach. In this pap...

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Bibliographic Details
Main Authors: Chao-Wei Chou, Jiann-Horng Lin, Chorng-Horng Yang, Hsien-Leing Tsai, Ya-Hui Ou
Format: Conference Proceeding
Language:English
Subjects:
Convergence
Genetic algorithms
Indexes
Markov chain
Markov processes
Optimization
Particle swarm optimization
particle swarm optimizer
Vectors
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Summary:The Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. As far as our investigation, most of the relevant researches are based on computer simulations and seldom 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 this paper, which contains all the information needed for the future evolution. Then the memory-less property of the state defined in this paper is investigated. Finally, by using the concept of the state and suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov chain is established.
DOI:10.1109/IBICA.2012.59