scholarly journals Efficient Stochastic Model for Operational Availability Optimization of Cooling Tower using Metaheuristic Algorithms

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Ashish Kumar ◽  
Monika Saini ◽  
Nivedita Gupta ◽  
Deepak Sinwar ◽  
Dilbag Singh ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Cooling Tower ◽  
Metaheuristic Algorithms

A robust fuzzy stochastic model for the responsive-resilient inventory-location problem: comparison of metaheuristic algorithms

2021 ◽  
Author(s):  
Sina Nayeri ◽  
Mahdieh Tavakoli ◽  
Mehrab Tanhaeean ◽  
Fariborz Jolai
Keyword(s):  
Stochastic Model ◽  
Location Problem ◽  
Metaheuristic Algorithms ◽  
Problem Comparison

scholarly journals T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 959
Author(s):  
Antonio Barrera ◽  
Patricia Román-Román ◽  
Francisco Torres-Ruiz
Keyword(s):  
Stochastic Model ◽  
Diffusion Process ◽  
Model Simulation ◽  
Likelihood Estimation ◽  
Real Data ◽  
Estimation Procedure ◽  
Metaheuristic Algorithms ◽  
Mathematical Treatment ◽  
Linear Differential ◽  
Lognormal Diffusion Process

The main objective of this work is to introduce a stochastic model associated with the one described by the T-growth curve, which is in turn a modification of the logistic curve. By conveniently reformulating the T curve, it may be obtained as a solution to a linear differential equation. This greatly simplifies the mathematical treatment of the model and allows a diffusion process to be defined, which is derived from the non-homogeneous lognormal diffusion process, whose mean function is a T curve. This allows the phenomenon under study to be viewed in a dynamic way. In these pages, the distribution of the process is obtained, as are its main characteristics. The maximum likelihood estimation procedure is carried out by optimization via metaheuristic algorithms. Thanks to an exhaustive study of the curve, a strategy is obtained to bound the parametric space, which is a requirement for the application of various swarm-based metaheuristic algorithms. A simulation study is presented to show the validity of the bounding procedure and an example based on real data is provided.

A Fruitful Stochastic Model

1964 ◽  
Vol 9 (7) ◽  
pp. 273-276
Author(s):  
ANATOL RAPOPORT
Keyword(s):  
Stochastic Model

A Stochastic Model for Evolution of Altruistic Genes

1996 ◽  
Vol 6 (4) ◽  
pp. 445-453 ◽  
Author(s):  
Roberta Donato
Keyword(s):  
Stochastic Model

A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Stochastic Model ◽  
Gaussian Process ◽  
Covariance Function ◽  
Neoplastic Disease ◽  
Disease Phenotype ◽  
Probabilistic Representation ◽  
Temporal Behaviour ◽  
Continuous Trait ◽  
Continuous Parameter ◽  
Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

Stochastic Model of Traffic Jam and Traffic Signal Control

2011 ◽  
Vol 131 (2) ◽  
pp. 303-310
Author(s):  
Ji-Sun Shin ◽  
Cheng-You Cui ◽  
Tae-Hong Lee ◽  
Hee-hyol Lee
Keyword(s):  
Stochastic Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam

Application of Ozone to Spraying Water in Cooling Tower of Transformer

2018 ◽  
Vol 138 (12) ◽  
pp. 939-947
Author(s):  
Hideo Hashimoto ◽  
Koh Hashiguchi ◽  
Hideki Fukui ◽  
Yusaku Ono ◽  
Mika Nakatoge ◽  
...  
Keyword(s):  
Cooling Tower
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