Akademik Veri Yönetim Sistemi
JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 2025 (ESCI, TRDizin)
Numerous studies on the statistical inferences of the Weibull distribution's parameters have been performed because it is among the most well-known and widely applied distributions in several fields, including lifetime studies and reliability. Although maximum likelihood is a widely used method in the estimation of unknown parameters, estimating the parameters by maximizing the likelihood function is very challenging for some distributions, like the three-parameter Weibull distribution. The Particle Swarm Optimization (PSO) algorithm is examined in order to address this issue and achieve improved outcomes. However, different parameter values for the algorithm need to be adjusted to achieve good results and increase the performance of PSO. In this context, it is very important to determine the inertia weight, which significantly affects the search process. As a novelty in this paper, chaotic maps for the inertia weight, which is the factor affecting the convergence of the PSO, are examined in detail for the estimation of different parameter values of the three-parameter Weibull distribution. The effectiveness of the suggested method is investigated by a thorough Monte-Carlo simulation analysis. The simulation findings demonstrate that the proposed chaotic map approach outperforms the classic linear decreasing inertia weights.