A Novel Global Optimization Method – Genetic Pattern Search

2010 ◽  
Vol 44-47 ◽  
pp. 3240-3244 ◽  
Author(s):  
Yu Dong Zhang ◽  
Le Nan Wu ◽  
Yuan Kai Huo ◽  
Shui Hua Wang
Keyword(s):  
Global Optimization ◽  
Computation Time ◽  
Optimization Method ◽  
Pattern Search ◽  
Test Functions ◽  
Global Optimization Method ◽  
Minimal Points ◽  
Genetic Pattern ◽  
Two Phases ◽  
Coarse Search

A novel global optimization method is proposed to find global minimal points more effectively and quickly. The new algorithm is based on both genetic algorithms (GA) and pattern search (PS) algorithms, thus, we have named it genetic pattern search. The procedure involves two-phases: First, GA executes a coarse search, PS then executes a fine search. Experiments on four different test functions (consisting of Hump, Powell, Rosenbrock, and Woods) demonstrate that this proposed new algorithm is superior to improved GA and improved PS with respect to success rate and computation time. Therefore, genetic pattern search is an effective and viable global optimization method.

scholarly journals Genetic Pattern Search and Its Application to Brain Image Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yudong Zhang ◽  
Shuihua Wang ◽  
Genlin Ji ◽  
Zhengchao Dong
Keyword(s):  
Global Optimization ◽  
Brain Mri ◽  
Global Solutions ◽  
Optimization Method ◽  
Pattern Search ◽  
The Novel ◽  
Global Optimization Method ◽  
Limited Region ◽  
Minimal Points ◽  
Genetic Pattern

A novel global optimization method, based on the combination of genetic algorithm (GA) and generalized pattern search (PS) algorithm, is proposed to find global minimal points more effectively and rapidly. The idea lies in the facts that GA tends to be quite good at finding generally good global solutions, but quite inefficient in finding the last few mutations for the absolute optimum, and that PS is quite efficient in finding absolute optimum in a limited region. The novel algorithm, named as genetic pattern search (GPS), employs the GA as the search method at every step of PS. Experiments on five different classical benchmark functions (consisting of Hump, Powell, Rosenbrock, Schaffer, and Woods) demonstrate that the proposed GPS is superior to improved GA and improved PS with respect to success rate. We applied the GPS to the classification of normal and abnormal structural brain MRI images. The results indicate that GPS exceeds BP, MBP, IGA, and IPS in terms of classification accuracy. This suggests that GPS is an effective and viable global optimization method and can be applied to brain MRI classification.

scholarly journals Design of dimensionally stable composites using efficient global optimization method

2016 ◽  
Vol 233 (2) ◽  
pp. 156-168 ◽  
Author(s):  
Levent Aydin ◽  
Olgun Aydin ◽  
H Seçil Artem ◽  
Ali Mert
Keyword(s):  
Global Optimization ◽  
Communication Systems ◽  
Optimization Problems ◽  
Material Design ◽  
Optimization Method ◽  
Pattern Search ◽  
Efficient Global Optimization ◽  
Global Optimization Method ◽  
Moisture Expansion ◽  
Design And Optimization

Dimensionally stable material design is an important issue for space structures such as space laser communication systems, telescopes, and satellites. Suitably designed composite materials for this purpose can meet the functional and structural requirements. In this paper, it is aimed to design the dimensionally stable laminated composites by using efficient global optimization method. For this purpose, the composite plate optimization problems have been solved for high stiffness and low coefficients of thermal and moisture expansion. Some of the results based on efficient global optimization solution have been verified by genetic algorithm, simulated annealing, and generalized pattern search solutions from the previous studies. The proposed optimization algorithm is also validated experimentally. After completing the design and optimization process, failure analysis of the optimized composites has been performed based on Tsai–Hill, Tsai–Wu, Hoffman, and Hashin–Rotem criteria.

scholarly journals The Coplane Analysis Technique for Three-Dimensional Wind Retrieval Using the HIWRAP Airborne Doppler Radar

2015 ◽  
Vol 54 (3) ◽  
pp. 605-623 ◽  
Author(s):  
Anthony C. Didlake ◽  
Gerald M. Heymsfield ◽  
Lin Tian ◽  
Stephen R. Guimond
Keyword(s):  
Global Optimization ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Optimization Method ◽  
Vertical Wind ◽  
Wind Component ◽  
Global Optimization Method ◽  
Analysis Technique ◽  
Radar Measurements ◽  
Cross Track

AbstractThe coplane analysis technique for mapping the three-dimensional wind field of precipitating systems is applied to the NASA High-Altitude Wind and Rain Airborne Profiler (HIWRAP). HIWRAP is a dual-frequency Doppler radar system with two downward-pointing and conically scanning beams. The coplane technique interpolates radar measurements onto a natural coordinate frame, directly solves for two wind components, and integrates the mass continuity equation to retrieve the unobserved third wind component. This technique is tested using a model simulation of a hurricane and compared with a global optimization retrieval. The coplane method produced lower errors for the cross-track and vertical wind components, while the global optimization method produced lower errors for the along-track wind component. Cross-track and vertical wind errors were dependent upon the accuracy of the estimated boundary condition winds near the surface and at nadir, which were derived by making certain assumptions about the vertical velocity field. The coplane technique was then applied successfully to HIWRAP observations of Hurricane Ingrid (2013). Unlike the global optimization method, the coplane analysis allows for a transparent connection between the radar observations and specific analysis results. With this ability, small-scale features can be analyzed more adequately and erroneous radar measurements can be identified more easily.

Sign in / Sign up

Export Citation Format

Share Document