scholarly journals Inverse Analysis of the Partitioning Deformation Modulusof High-Arch Dams Based on Quantum Genetic Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yantao Zhu ◽  
Xinqiang Niu ◽  
Jimin Wang ◽  
Chongshi Gu ◽  
Erfeng Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Inverse Analysis ◽  
Fitness Function ◽  
Deformation Modulus ◽  
Operating Conditions ◽  
Hydraulic Structures ◽  
Arch Dams ◽  
Quantum Genetic Algorithm ◽  
Reservoir Basin ◽  
High Arch Dams

The physical and mechanical parameters of hydraulic structures in complicated operating conditions often change over time. Updating these parameters in a timely manner is important to comprehend the operating behaviors and monitor the safety of hydraulic structures. Conventional inverse analysis methods can only generate inversions on the comprehensive deformation modulus of concrete dam structures, which contradict practical conditions. Based on the researches on conventional reversion methods of the deformation modulus of the dam body, foundation, and reservoir basin, the objective fitness function is established in this paper according to engineering-measured data and finite element simulation results. The quantum genetic algorithm has high global search efficiency and population diversity. A mechanical parameter inversion of high-arch dams is built from the intelligent optimization of an established algorithm by applying the quantum genetic algorithm. The proposed algorithm is tested to be feasible and valid for practical engineering projects and therefore shows scientific and practical application values.

scholarly journals Fractional Dynamics of Genetic Algorithms Using Hexagonal Space Tessellation

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
J. A. Tenreiro Machado
Keyword(s):  
Genetic Algorithm ◽  
Distance Measure ◽  
Dimensional Space ◽  
Fitness Function ◽  
Operating Conditions ◽  
Model Parameters ◽  
Fractional Dynamics ◽  
Fourier Domain ◽  
Two Dimensional ◽  
Percolation Phenomena

The paper formulates a genetic algorithm that evolves two types of objects in a plane. The fitness function promotes a relationship between the objects that is optimal when some kind of interface between them occurs. Furthermore, the algorithm adopts an hexagonal tessellation of the two-dimensional space for promoting an efficient method of the neighbour modelling. The genetic algorithm produces special patterns with resemblances to those revealed in percolation phenomena or in the symbiosis found in lichens. Besides the analysis of the spacial layout, a modelling of the time evolution is performed by adopting a distance measure and the modelling in the Fourier domain in the perspective of fractional calculus. The results reveal a consistent, and easy to interpret, set of model parameters for distinct operating conditions.

A Improved Bus Timetable Scheduling Model Using Quantum Genetic Algorithm Based on Penalty Strategy

2012 ◽  
Vol 253-255 ◽  
pp. 1406-1409 ◽  
Author(s):  
Xin Lai Tang ◽  
Shu Hong Yang
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Optimal Parameters ◽  
Quantum Genetic Algorithm ◽  
Scheduling Model ◽  
Bus Scheduling ◽  
Low Efficiency ◽  
The Cost ◽  
Bus Operator ◽  
Penalty Strategy

Considering the influence that the cycles of signal lamp have on the waiting time, a bus scheduling model is presented in this paper based on the trade-off between the cost of bus operator and benefits of passengers. In order to handle with the low efficiency brought about by the refused strategy, a new fitness function is designed according to penalty strategy, and then traditional genetic algorithm is replaced by quantum genetic algorithm to accelerate the search of optimal parameters further. The results of experiment show that the presented method is effective.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

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