Performance Analysis of Simulated Annealing Cooling Schedules in the Context of Dense Image Matching

Mahdi, Walid; Medjahed, Seyyid Ahmed; Ouali, Mohammed

doi:10.13053/cys-21-3-2553

Cited by 18 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…which is very common in several other SAA implementations (Peprah et al, 2017;Mahdi et al, 2017;Cabrera et al, 2014;Khairuddin and Zainuddin, 2019).…”

Section: The Geophysical Inversionmentioning

confidence: 96%

Simulated Annealing for volcano muography

Vesga-Ramirez

Sanabria-Gómez

Sierra-Porta

et al. 2021

Journal of South American Earth Sciences

View full text Add to dashboard Cite

We discuss the geophysical inversion methodology for volcanic muography based on the Simulated Annealing algorithm, using a semi-empirical model of the muon flux reaching the volcano, its surrounding topography and a framework for the energy loss of muons in rock. We determined the minimum muon energy-as function of the arrival direction-needed to cross the volcanic building, the emerging integrated flux of muons and the density profile inside a model of Cerro Machín volcano (Tolima, Colombia) within a maximum error of 1% concerning the theoretical model.

show abstract

“…which is very common in several other SAA implementations (Peprah et al, 2017;Mahdi et al, 2017;Cabrera et al, 2014;Khairuddin and Zainuddin, 2019).…”

Section: The Geophysical Inversionmentioning

confidence: 96%

Simulated Annealing for volcano muography

Vesga-Ramirez

Sanabria-Gómez

Sierra-Porta

et al. 2021

Journal of South American Earth Sciences

View full text Add to dashboard Cite

show abstract

“…Logarithmic cooling, Geometrical cooling, Linear cooling, Adaptive and Arithmetic-Geometric cooling schedules [13] are cooling techniques, the fastest one of them is Geometrical cooling, its formula as following:…”

Section: Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Benchmarking Meta-heuristic Optimization

Nasr¹,

Farouk²,

Mohamedeen³

et al. 2020

IJANA

View full text Add to dashboard Cite

Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems. In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other. We will go through the Genetic Algorithm, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing. They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm's result is close to the optimal result. Other points of evaluation are discussed in depth in later sections.

show abstract

“…Maximum iteration : imax ; 3. Geometrical cooling coefficient: α; it is a constant between 0 and 1; to have as low decrease of temperature, it is better to set α closer to 1 [18].…”

Section: Simulated Annealing:-mentioning

confidence: 99%