Estimating the Survival Function of Mixture Inverse Rayleigh and Inverse Gompertz Using a New Hybrid Meta Heuristic Algorithm

Shahad K. Taher; Bayda Atiya  Kalaf

doi:10.30526/39.3.4233

Authors

Shahad K. Taher Department of Mathematics, College of Education for Pure Sciences (Ibn Al-Haitham), University of Baghdad, Baghdad, Iraq https://orcid.org/0009-0007-7508-8333
Bayda Atiya Kalaf Department of Mathematics, College of Education for Pure Sciences (Ibn Al-Haitham), University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0003-1136-0055

DOI:

https://doi.org/10.30526/39.3.4233

Keywords:

Mixture distribution, Particle Swarm Optimization, Monkey algorithm, survival function

Abstract

This paper proposes a new compound model involving the inverse Rayleigh and inverse Gompertz distributions with four versatile parameters. Some of the statistical measures of the newly suggested distribution are derived, including the survival function, hazard function, cumulative distribution function, moments about the origin, mode, and median. To estimate the survival function from the distribution parameters, a new hybrid optimization algorithm (PSOMA) is introduced. The robust algorithm combines the strengths of Particle Swarm Optimization (PSO) and Monkey Optimization (MO). A simulated experiment following the mean square error (MSE) metric is conducted to compare the performance of PSOMA with the standard PSO and MO algorithms. The results are that the new hybrid algorithm can accurately estimate the survival function with virtually perfect precision under simulated conditions. Besides, PSOMA consistently produces lower mean square errors than competing methods, confirming its improved predictive capability, robustness, and reliability for a broad variety of survival analysis tasks.

Author Biographies

Shahad K. Taher, Department of Mathematics, College of Education for Pure Sciences (Ibn Al-Haitham), University of Baghdad, Baghdad, Iraq

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Bayda Atiya Kalaf , Department of Mathematics, College of Education for Pure Sciences (Ibn Al-Haitham), University of Baghdad, Baghdad, Iraq

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