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Abstract

This work presents a novel approach to enhancing the rate of occurrence of non-homogeneous Poisson processes (NHPP) by utilizing the Gompertz distribution as the rate of occurrence. The primary objective of this study is to determine the parametersof the new process using both traditional methods andintelligent technology, specifically particleswarm optimization (PSO). Additionally, the study aims to estimate the reliability function ofthe process. The suggested model is simulatedto achieve these goals, and theresults are compared among various estimation techniques to identify the most accurate estimator. The study demonstrates that when predicting the time rate of occurrence oftheproposed Gompertz process and its reliability function, the PSO algorithm outperforms other approaches. Furthermore, this research showcases a practical application utilizing real data from the Mosul power facility. Specifically, the data pertains to the stoppage times of two consecutive units of the Mosul Dam power stations from January 1st, 2021 to January 1st, 2022.Overall, this study introduces a novel process based on the Gompertz distribution to improve the rate of occurrence of NHPP. It employsparticle swarm optimization to calculate the process parameters and estimate the reliability function. The superiority of the PSO algorithm is demonstrated through comprehensive comparisons. The practical application using data from the Mosul power facility further validates the effectiveness of the proposed approach.

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