scholarly journals Optimization Design and Calculation of Mooring System Parameters based on Genetic Algorithm

2018 ◽  
Vol 153 ◽  
pp. 07006
Author(s):  
Liang-yu Xia ◽  
Li Zhi ◽  
Ling Chen ◽  
Yue Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Equilibrium Model ◽  
Optimization Design ◽  
Single Point ◽  
Mooring System ◽  
Crossover Operator ◽  
Multi Objective Optimization ◽  
System Parameters ◽  
The Stability

Based on the static equilibrium model of mooring system parameters, the multi - objective optimization model is established and the genetic algorithm is introduced as the optimization algorithm, which improves the crossover operator. Taking the example of a single point mooring system, concerned optimization design and calculation has been done. Compared with the general genetic algorithm, the result of this algorithm is superior, significantly improving the stability of the mooring system.

An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering

2014 ◽  
Vol 962-965 ◽  
pp. 2903-2908
Author(s):  
Yun Lian Liu ◽  
Wen Li ◽  
Tie Bin Wu ◽  
Yun Cheng ◽  
Tao Yun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Crossover Operator ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Great Ability ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 3 benchmark functions and 1 engineering optimization problems, and comparing with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search. Keywords: multi-objective optimization;genetic algorithm;constrained optimization problem;engineering application

Analysis of Vehicle Dynamic Equilibrium Points with 3-DOF Based on Genetic Algorithm

2014 ◽  
Vol 608-609 ◽  
pp. 721-725
Author(s):  
Rong Li ◽  
Wei Min Li
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Dynamics ◽  
Optimization Design ◽  
Degrees Of Freedom ◽  
Dynamic Equilibrium ◽  
Equilibrium Points ◽  
Vehicle Handling ◽  
Dynamic Modification ◽  
The Stability ◽  
Three Degrees Of Freedom

To further study the stability of vehicle dynamics, a vehicle handling stability’s nonlinear model (including longitudinal, lateral and yaw movement three degrees of freedom) was established. Genetic algorithm was proposed for the vehicle dynamics system’s equilibrium points with 3-DOF. This algorithm solves the problem that cannot be solved through the traditional analytic algorithms and numerical methods. Comparing with the existing research results, the feasibility of solving the equilibrium point by the genetic algorithm is verified. It provides the theoretical foundation for dynamic modification and optimization design of powertrain.

Multi-Objective Optimization Design of Screw Conveyor Using Genetic Algorithm

2013 ◽  
Vol 732-733 ◽  
pp. 402-406
Author(s):  
Duan Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Design ◽  
Optimization Problem ◽  
Machine Design ◽  
Screw Conveyor ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Mathematical Model

The weight minimum and drive efficiency maxima1 of screw conveyor were considered as double optimizing objects in this paper. The mathematical model of the screw conveyor has been established based on the theory of the machine design, and the genetic algorithm was adopted to solving the multi-objective optimization problem. The results show that the mass of spiral shaft reduces 13.6 percent, and the drive efficiency increases 6.4 percent because of the optimal design based on genetic algorithm. The genetic algorithm application on the screw conveyor optimized design can provided the basis for designing the screw conveyor.

The System of Spindle Optimization Design Based on GA

2012 ◽  
Vol 466-467 ◽  
pp. 773-777 ◽  
Author(s):  
Peng Jia Wang ◽  
Chen Guang Guo ◽  
Yong Xian Liu ◽  
Zhong Qi Sheng
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Dynamic Property ◽  
Design Variable ◽  
Optimization Design ◽  
Search Space ◽  
Mutation Operator ◽  
Crossover Operator ◽  
Selection Operator ◽  
The Mathematical Model

Aiming at the optimization design of spindle, this paper introduces deflection constraint, strength constraint, corner constraint, cutting force constraint, the limit of torsional deflection, boundary constraint of design variable, dynamic property constraint , realizes the expression of the mathematical model of the spindle optimization design. Through the introduction of the real number code rule, the selection operator is built by adopting the optimum maintaining tactics and proportional selection, the crossover operator is built by using the method of arithmetic crossover and the mutation operator is built by using the method of uniform mutation. In the platform of VC++, the system of spindle optimization design based on GA is built. The analysis of the example shows that using the genetic algorithm to optimize the spindle can ensure the convergence of the optimization course, expand the search space, and the effect of optimization is obvious.

Sign in / Sign up

Export Citation Format

Share Document