ParametersEstimation of a Proposed Non-Homogeneous Poisson Process and Estimation of the Reliability Function Using the Gompertz Process: A Comparative Analysis of Artificially Intelligent and Traditional Methods

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This work presents a novel approach to enhancing the rate of occurrence of non-homogeneousPoisson processes (NHPP) by utilizing the Gompertz distribution as the rate of occurrence. The primary objectiveof this study is to determine the parameters of the new process using both traditional methods and intelligenttechnology, specifically particle swarm optimization (PSO). Additionally, the study aims to estimate the reliabilityfunction of the process. The suggested model is simulated to achieve these goals, and the results are comparedamong various estimation techniques to identify the most accurate estimator. The study demonstrates that whenpredicting the time rate of occurrence of the proposed Gompertz process and its reliability function, the PSOalgorithm outperforms other approaches. Furthermore, this research showcases a practical application utilizing realdata from the Mosul power facility. Specifically, the data pertains to the stoppage times of two consecutive units ofthe Mosul Dam power stations from January 1st, 2021 to January 1st, 2022. Overall, this study introduces a novelprocess based on the Gompertz distribution to improve the rate of occurrence of NHPP. It employs particle swarmoptimization to calculate the process parameters and estimate the reliability function. The superiority of the PSOalgorithm is demonstrated through comprehensive comparisons. The practical application using data from theMosul power facility further validates the effectiveness of the proposed approach.