scholarly journals Research on Flight Trajectory Optimization Based on Quantum Genetic Algorithm

2020 ◽  
Vol 1549 ◽  
pp. 022074
Author(s):  
Yibo Li ◽  
Shuaitao Qin ◽  
Lei Jing
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Optimization ◽  
Flight Trajectory ◽  
Quantum Genetic Algorithm

scholarly journals FSQGA based 3D complexity wellbore trajectory optimization

2018 ◽  
Vol 73 ◽  
pp. 79 ◽  
Author(s):  
Linxiu Sha ◽  
Zhongqi Pan
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Optimization ◽  
High Speed ◽  
Complex Structure ◽  
Fibonacci Sequence ◽  
Oil Field ◽  
Quantum Genetic Algorithm ◽  
Drilling Cost ◽  
Cost Efficient

Determination of the trajectory of a complex wellbore is very challenging due to the variety of possible well types, as well as the numerous complicated drilling variables and constraints. The well type could be directional wells, cluster wells, horizontal wells, extended reach wells, redrilling wells, and complex structure wells, etc. The drilling variables and constraints include wellbore length, inclination hold angles, azimuth angles, dogleg severity, true vertical depths, lateral length, casing setting depths, and true vertical depth. In this paper, we propose and develop an improved computational model based on Fibonacci sequence to adjust the quantum rotation step in quantum genetic algorithm for achieving cost-efficient complex wellbore trajectories. By using Fibonacci sequence based quantum genetic algorithm (FSQGA) in a complex searching problem, we can find high-quality globally optimal solutions with high speed through a parallel process. The simulation results show that FSQGA can significantly reduce computation complexity, and reach minimum objection values faster. Meanwhile, minimization of the true measurement depth of complex wellbore trajectory in actual gas-oil field shows that the drilling cost can be reduced up to 4.65%. We believe this new algorithm has the potential to improve drilling efficiency, to reduce the drilling time and drilling cost in real-time wellbore trajectory control.

scholarly journals Quantum Genetic Terrain Algorithm (Q-GTA): A Technique to Study the Evolution of the Earth Using Quantum Genetic Algorithm

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 26
Author(s):  
Pranjal Sharma ◽  
Ankit Agarwal ◽  
Bhawna Chaudhary
Keyword(s):  
Genetic Algorithm ◽  
Isotopic Fractionation ◽  
Volcanic Eruptions ◽  
Temperature Characteristic ◽  
Isotopic Ratios ◽  
Mantle Lithosphere ◽  
Monitoring Networks ◽  
Temperature Changes ◽  
Quantum Genetic Algorithm ◽  
The Earth

In recent years, geologists have put in a lot of effort trying to study the evolution of Earth using different techniques studying rocks, gases, and water at different channels like mantle, lithosphere, and atmosphere. Some of the methods include estimation of heat flux between the atmosphere and sea ice, modeling global temperature changes, and groundwater monitoring networks. That being said, algorithms involving the study of Earth’s evolution have been a debated topic for decades. In addition, there is distinct research on the mantle, lithosphere, and atmosphere using isotopic fractionation, which this paper will take into consideration to form genes at the former stage. This factor of isotopic fractionation could be molded in QGA to study the Earth’s evolution. We combined these factors because the gases containing these isotopes move from mantle to lithosphere or atmosphere through gaps or volcanic eruptions contributing to it. We are likely to use the Rb/Sr and Sm/Nd ratios to study the evolution of these channels. This paper, in general, provides the idea of gathering some information about temperature changes by using isotopic ratios as chromosomes, in QGA the chromosomes depict the characteristic of a generation. Here these ratios depict the temperature characteristic and other steps of QGA would be molded to study these ratios in the form of temperature changes, which would further signify the evolution of Earth based on the study that temperature changes with the change in isotopic ratios. This paper will collect these distinct studies and embed them into an upgraded quantum genetic algorithm called Quantum Genetic Terrain Algorithm or Quantum GTA.

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