Use of Genetic Algorithm for Phase Optimization at Intersections with Minimization of Vehicle and Pedestrian Delays

2011 ◽  
Vol 2264 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Zengyi Yang ◽  
Rahim F. Benekohal
Keyword(s):  
Genetic Algorithm ◽  
Phase Optimization ◽  
Pedestrian Delays

Quantized phase optimization of two-dimensional Fourier kinoforms by a genetic algorithm

1995 ◽  
Vol 20 (7) ◽  
pp. 752 ◽  
Author(s):  
N. Yoshikawa ◽  
M. Itoh ◽  
T. Yatagai
Keyword(s):  
Genetic Algorithm ◽  
Two Dimensional ◽  
Phase Optimization

scholarly journals Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3118
Author(s):  
Qiushi Bi ◽  
Guoqiang Wang ◽  
Yongpeng Wang ◽  
Zongwei Yao ◽  
Robert Hall
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Trajectory Planning ◽  
Open Pit ◽  
Open Pit Mining ◽  
Second Phase ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Phase Optimization ◽  
Cable Shovel

As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of 20   s to 10   s after the first phase optimization, and the energy consumption per payload reduced by 13.28% after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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