Estimation of the parameters of the mathematical model of an equivalent diode of a photovoltaic panel using a continuous genetic algorithm

2022 ◽  
Vol 20 (4) ◽  
pp. 616-623
Author(s):  
Jhon Jairo Montano ◽  
Luis F. Grisales Norena ◽  
Andres Felipe Tobon ◽  
Daniel Gonzalez Montoya
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Photovoltaic Panel ◽  
The Mathematical Model ◽  
Continuous Genetic Algorithm

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

Genetic Algorithm Optimization of a Double Four-Bar Manipulator

2013 ◽  
Vol 834-836 ◽  
pp. 1323-1326
Author(s):  
Qi Jing Tang ◽  
Tie Shi Zhao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Structural Strength ◽  
Optimization Method ◽  
Installation Space ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Transmission Angle ◽  
The Mathematical Model

In order to optimize the dimension of a manipulator, the optimization requirements are analyzed. Then the mathematical model and optimization objectives are established. Next, the lengths of the manipulator are optimized by Matlab genetic algorithm optimization toolbox. The structural strength and bearing installation space are considered at the same time. The trajectory and transmission angle are compared. Finally, the lengths which meet the use requirements are obtained. This optimization method provides a reference for similar mechanism.

Intelligent Generating Examination Paper from Item Bank Based on Improved Genetic Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 3637-3640
Author(s):  
Li Feng ◽  
Qian Wu ◽  
Jing Shao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Computational Study ◽  
Item Bank ◽  
Test Paper ◽  
Improved Genetic Algorithm ◽  
Fitness Evaluation ◽  
Simulation Results ◽  
The Mathematical Model ◽  
Examination Paper

In this paper, we analyze the disadvantage of common generating test paper algorithm. An improved genetic algorithm (IGA) is proposed and used in auto-generating examination paper algorithm. We design the mathematical model of auto-generating test paper algorithm and improved the traditional GA fitness evaluation form. A computational study is carried out to verify the algorithm. Simulation results demonstrate that the performance of IGA can work efficiently than traditional ones.

scholarly journals Solution of Fractional Order System of Bagley-Torvik Equation Using Evolutionary Computational Intelligence

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Muhammad Asif Zahoor Raja ◽  
Junaid Ali Khan ◽  
Ijaz Mansoor Qureshi
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Mathematical Model ◽  
Fractional Order ◽  
Computational Intelligence ◽  
Pattern Search ◽  
Fractional Order System ◽  
Order System ◽  
Search Technique ◽  
The Mathematical Model

A stochastic technique has been developed for the solution of fractional order system represented by Bagley-Torvik equation. The mathematical model of the equation was developed with the help of feed-forward artificial neural networks. The training of the networks was made with evolutionary computational intelligence based on genetic algorithm hybrid with pattern search technique. Designed scheme was successfully applied to different forms of the equation. Results are compared with standard approximate analytic, stochastic numerical solvers and exact solutions.

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.

scholarly journals Research on the driving strategy of heavy-haul train based on improved genetic algorithm

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879101 ◽  
Author(s):  
Youneng Huang ◽  
Shuai Bai ◽  
Xianhong Meng ◽  
Huazhen Yu ◽  
Mingzhu Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Driving Safety ◽  
Sufficient Time ◽  
Improved Genetic Algorithm ◽  
Safe Operation ◽  
Multiple Parameters ◽  
Heavy Haul Train ◽  
Heavy Haul ◽  
The Mathematical Model

The driving safety of heavy-haul train is affected by the train’s traction weight, the length of train, the line profile, the line speed limit, and other factors. Generally, when the train is running on a continuously long and steep downgrade line, it needs using the circulating air braking to adjust speed. When it is braking, the brake wave is transmitted non-linearly along the direction of the train. When it is relieved, it must be ensured that there is sufficient time for the train to be inflated. Therefore, it is difficult to ensure the safe operation of the heavy-haul train. In this article, a new method of the train’s driving strategy based on improved genetic algorithm is proposed. First, a mathematical model for the operation of heavy-haul train is established with multiple parameters. Then, according to the improved genetic algorithm and the mathematical model of the heavy-haul train, the driving strategy of the chromosome of the train is studied. Finally, the driving curve which can ensure the safe running of the heavy-haul train can be obtained. By comparing the simulated driving curve with the actual one, the results show the effectiveness of the proposed method.

Research on Multi-Objective Transmission Planning in Electricity Markets

2012 ◽  
Vol 516-517 ◽  
pp. 1429-1432
Author(s):  
Yang Liu ◽  
Xu Liu ◽  
Feng Xian Cui ◽  
Liang Gao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Electricity Markets ◽  
Optimization Problem ◽  
Nsga Ii ◽  
Transmission Planning ◽  
Multi Objective ◽  
Complex Optimization ◽  
The Mathematical Model

Abstract. Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

Simulation of MH370 Actual Route Using Multiobjective Algorithms

2020 ◽  
pp. 77-99
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Indian Ocean ◽  
Southern Indian Ocean ◽  
Gulf Of Thailand ◽  
Multiobjective Genetic Algorithm ◽  
The Gulf Of Thailand ◽  
Real Scenario ◽  
The Mathematical Model ◽  
Multiobjective Algorithms

This chapter delivers the mathematical model to retrieve the definite route of MH370 and its debris, which is based on a multi-objective evolutionary algorithm. The chapter shows that the appropriate short route for Captian Zaharie to murder-suicide is the Gulf of Thailand, not in the Southern Indian Ocean, which is specified by 1000 iterations and 100 fitness. Needless to say that the MH370 path reclaimed from Inmarsat 3-F1 satellite data was not delivering the real scenario of MH370's vanishing, which is proving the multiobjective genetic algorithm.

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