Comparison of the Meta-Heuristic Algorithms for Maximum Likelihood Estimation of the Exponentially Modified Logistic Distribution

Generalized distributions have been studied a lot recently because of their flexibility and reliability in modeling lifetime data. The two-parameter Exponentially-Modified Logistic distribution is a flexible modified distribution that was introduced in 2018. It is regarded as a strong competitor for...

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Vydané v:Symmetry (Basel) Ročník 16; číslo 3; s. 259
Hlavní autori: Kasap, Pelin, Faouri, Adi Omaia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.03.2024
Predmet:
Algorithms
Cetacea
Comparative analysis
Computer simulation
Estimates
Flexibility
Genetic algorithms
Heuristic
Heuristic methods
Localization
Mathematical models
Mathematical optimization
Maximum likelihood estimates
Maximum likelihood estimation
Monte Carlo method
Monte Carlo simulation
Normal distribution
Numerical analysis
Optimization
Parameter modification
Particle swarm optimization
Survival analysis
Trigonometric functions
ISSN:2073-8994, 2073-8994
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Shrnutí:Generalized distributions have been studied a lot recently because of their flexibility and reliability in modeling lifetime data. The two-parameter Exponentially-Modified Logistic distribution is a flexible modified distribution that was introduced in 2018. It is regarded as a strong competitor for widely used classical symmetrical and non-symmetrical distributions such as normal, logistic, lognormal, log-logistic, and others. In this study, the unknown parameters of the Exponentially-Modified Logistic distribution are estimated using the maximum likelihood method. Five meta-heuristic algorithms, including the genetic algorithm, particle swarm optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and sine cosine algorithm, are applied in order to solve the nonlinear likelihood equations of the study model. The efficiencies of all maximum likelihood estimates for these algorithms are compared via an extensive Monte Carlo simulation study. The performance of the maximum likelihood estimates for the location and scale parameters of the Exponentially-Modified Logistic distribution developed with the genetic algorithm and grey wolf optimization algorithms is the most efficient among others, according to simulation findings. However, the genetic algorithm is two times faster than grey wolf optimization and can be considered better than grey wolf optimization considering the computation time criterion. Six real datasets are analyzed to show the flexibility of this distribution.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym16030259