On a sequential search strategy in global optimization problems

CALCOLO ◽  
1982 ◽  
Vol 19 (3) ◽  
pp. 321-334 ◽  
Author(s):  
F. Schoen
Keyword(s):  
Global Optimization ◽  
Search Strategy ◽  
Optimization Problems ◽  
Sequential Search ◽  
Sequential Search Strategy

Estimation of Locations of Densely Distributed Subjects Using NMF with Nonpixel Information

2014 ◽  
Vol 18 (4) ◽  
pp. 632-647
Author(s):  
Yusuke Kubo ◽  
◽  
Masao Kubo ◽  
Hiroshi Sato ◽  
Akira Namatame
Keyword(s):  
Real Time ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Search Strategy ◽  
Nonnegative Matrix ◽  
Small Region ◽  
High Availability ◽  
Sequential Search ◽  
Time Estimates ◽  
Sequential Search Strategy

We propose a method that uses a large number of digital photographs to produce highly accurate estimates of the locations of subjects that have attracted a crowd’s attention. Recently, a very active area of research has been to use humans as sensors in realworld observations that require a large amount of data. Some of these studies have attempted to produce real-time estimates of the subjects that are attracting a crowd’s attention by quickly collecting a large number of photographs. These studies are based on the assumption that, when photographers encounter interesting events, they take pictures. Some of the proposed methods realize high availability by using only photographing information, which includes information about location and azimuth of the camera and it is automatically embedded into photograph. Since this data is very small compared to that of the pixel information, the load on the communication infrastructure is reduced. However, there are problems with the accuracy when there are many attractive subjects in a small region, and they cannot be found with traditional methods that use a sequential search strategy. The proposed method overcomes this problem by applying nonnegative matrix factorization (NMF) to the estimated location of each subject. We verified the effectiveness of this by computational experiments and an experiment under a realistic environment.

A Hybrid Algorithm Based on Flower Pollination Algorithm and Electro Search for Global Optimization

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Md Fadil Md Esa ◽  
Noorfa Haszlinna Mustaffa ◽  
Nor Haizan Mohamed Radzi
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
High Performance ◽  
Search Strategy ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Method ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Local Search Strategy

In this paper, we have presented a new hybrid optimization method called hybrid Electro-Search algorithm (Eo) and Flower Pollination Optimization Algorithm (FPA) which introduces Eo to FPA. EO-FPA combines the merits of both Eo and FPA by designing on the local-search strategy from Eo and global-search strategy from FPA. The results of the experiments performed with twenty-two well-known benchmark functions show that the proposed algorithm possesses outstanding performance in statistical merit as compared to the original and variant FPA. It is proven that the EO-FPA algorithm requires better formulation to achieve efficiency and high performance to work out with global optimization problems.

Dynamic Cell Structures for the Evaluation of Keypoints in Facial Images

1997 ◽  
Vol 08 (01) ◽  
pp. 27-39 ◽  
Author(s):  
R. Herpers ◽  
L. Witta ◽  
J. Bruske ◽  
G. Sommer
Keyword(s):  
Search Strategy ◽  
Structural Information ◽  
Data Driven ◽  
Average Error ◽  
Sequential Search ◽  
Facial Landmarks ◽  
Model Driven ◽  
Cell Structures ◽  
Dynamic Cell ◽  
Sequential Search Strategy

In this contribution Dynamic Cell Structures (DCS network) are applied to classify local image structures at particular facial landmarks. The facial landmarks such as the corners of the eyes or intersections of the iris with the eyelid are computed in advance by a combined model and data driven sequential search strategy. To reduce the detection error after the processing of the sequential search strategy, the computed image positions are verified applying a DCS network. The DCS network is trained by supervised learning with feature vectors which encode spatially arranged edge and structural information at the keypoint position considered. The model driven localization as well as the data driven verification are based on steerable filters, which build a representation comparable with one provided by a receptive field in the human visual system. We apply a DCS based classifier because of its ability to grasp the topological structure of complex input spaces and because it has proved successful in a number of other classification tasks. In our experiments the average error resulting from false positive classifications is less than 1%.

Hybrid orthogonal CMAES for solving global optimization problems

2013 ◽  
Vol 32 (4) ◽  
pp. 981-985
Author(s):  
Ya-fei HUANG ◽  
Xi-ming LIANG ◽  
Yi-xiong CHEN
Keyword(s):  
Global Optimization ◽  
Optimization Problems
Sign in / Sign up

Export Citation Format

Share Document