Particle Swarm Optimization Applied to Parameter Estimation of the Four-Parameter Burr Ⅲ Distribution

摘要

The Burr III distribution is a very popular distribution for modeling real data in terms of risk, reliability, and process capability, and thus the estimation of its parameters is essential in most real applications. The classical estimation methods, such as maximum likelihood and least squares, are often used to estimate the parameters of the Burr III distribution. However, maximizing the likelihood function developed for the parameter estimation of the four-parameter Burr type III distribution is a quite difficult problem. Hence, the heuristic approaches must be used to discover good solutions. Particle swarm optimization (PSO) is one of the heuristic approaches, which is a population-based technique developed from swarm intelligence. This paper proposes an alternative parameter estimation method for Burr III distribution using the PSO heuristic approach. Simulation results show that the PSO approach provides accurate estimates and the PSO method is satisfactory for the parameter estimation of Burr III distribution.