scholarly journals Array Processing and Fast Optimization Algorithms for Distorted Circular Contour Retrieval

2007 ◽  
Vol 2007 (1) ◽  
Author(s):  
Julien Marot ◽  
Salah Bourennane
Keyword(s):  
Array Processing ◽  
Optimization Algorithms ◽  
Circular Contour ◽  
Fast Optimization

scholarly journals Fast optimization algorithms and the cosmological constant

2017 ◽  
Vol 96 (10) ◽  
Author(s):  
Ning Bao ◽  
Raphael Bousso ◽  
Stephen Jordan ◽  
Brad Lackey
Keyword(s):  
Cosmological Constant ◽  
Optimization Algorithms ◽  
Fast Optimization

Immune electron microscopy of haemocyanins from two southern african whelks

1978 ◽  
Vol 36 (2) ◽  
pp. 158-159
Author(s):  
Linda M. Stannard ◽  
Margaret Lennon
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Outer Ring ◽  
Immune Electron Microscopy ◽  
Intertidal Zones ◽  
Central Belt ◽  
Cape Peninsula ◽  
Circular Contour

Burnupena cincta and Fusus verruculatus are two whelks which inhabit the intertidal zones of the Cape Peninsula shore. Their respiratory pigments, or haemocyanins, are morphologically similar in structure (Figs. 1 and 2) and appear in the electron microscope as short cylindrical rods about 34 nm in diameter and 36 nm high. Viewed side-on the molecules show regular banding suggesting a structure composed of six equidistant rings of sub-units. Occasionally the particles have the appearance of possessing a central “belt” in the position of the 3rd and 4th rows of sub-units. End-on views of the haemocyanin molecules show a circular contour with a dense outer ring and a less dense inner ring in which 10 definite sub-units may frequently be distinguished. A number of molecules display an extra central inner component which appears either as a diffuse plug or as a discrete ring-shaped core ± 8 nm in diameter.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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