Optimization of Transit Hub Terminals Based on Improved Adaptive Genetic Algorithm

2014 ◽  
Vol 488-489 ◽  
pp. 942-946
Author(s):  
Chun Mei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Population Diversity ◽  
Convergence Speed ◽  
Adaptive Genetic Algorithm ◽  
Allocation Model ◽  
Slow Convergence ◽  
Optimized Allocation ◽  
The Individual ◽  
Crossover And Mutation

In this paper, how to design the layout of transit hub terminals is discussed, and an optimized allocation model about bus lines and bus terminals is established. In order to address the slow convergence of adaptive genetic algorithm, an index that indicates population diversity degree is introduced to adjust the individual crossover and mutation rate. This improved adaptive genetic algorithm is applied for the allocation model and an example is used to validate its efficiency. Results show that it is a promising approach and can improve the convergence speed.

Reactive Power Optimization Based on CAGA Algorithm

2012 ◽  
Vol 616-618 ◽  
pp. 2210-2213
Author(s):  
Li Jun Chen ◽  
Ran Ran Hai ◽  
Ya Hong Zhang ◽  
Gang Gang Xu
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Cloud Model ◽  
Optimal Solution ◽  
Population Diversity ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Global Optimal ◽  
Crossover And Mutation

Reactive power optimization is a typical high-dimensional, nonlinear, discontinuous problem. Traditional Genetic algorithm(GA) exists precocious phenomenon and is easy to be trapped in local minima. To overcome this shortcoming, this article will introduce cloud model into Adaptive Genetic Algorithm (AGA), adaptively adjust crossover and mutation probability according to the X-condition cloud generator to use the randomness and stable tendency of droplets in cloud model. The article proposes the cloud adaptive genetic algorithm(CAGA) ,according to the theory, which probability values have both stability and randomness, so, the algorithm have both rapidity and population diversity. Considering minimum network loss as the objective function, make the simulation in standard IEEE 14 node system. The results show that the improved CAGA can achieve a better global optimal solution compared with GA and AGA.

Path Planning of Stacker for Automated Building Materials Warehouse Based on the Improved Adaptive Genetic Algorithm

2013 ◽  
Vol 325-326 ◽  
pp. 1475-1478
Author(s):  
Shen Li
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Building Materials ◽  
Convergence Speed ◽  
Adaptive Genetic Algorithm ◽  
Work Efficiency ◽  
Early Maturity ◽  
Slow Convergence ◽  
Simulation Results

In this paper, the improved adaptive genetic algorithm has been presented, which not only can overcome the early maturity and slow convergence speed of traditional genetic algorithm, and greatly improve the efficiency of genetic algorithm. This method can improve the quality of stacker path planning and work efficiency for modern automated building materials warehouse. Using the genetic algorithm toolbox of Matlab, the simulation results can further verified the feasibility and effectiveness of this method.

Application of Control Parameters Optimization of CNC Servo System Based on Self-Adaptive Genetic Algorithm

2011 ◽  
Vol 239-242 ◽  
pp. 2847-2850
Author(s):  
Gui Rong Dong ◽  
Peng Bing Zhao
Keyword(s):  
Genetic Algorithm ◽  
Pid Control ◽  
Population Diversity ◽  
Parameters Optimization ◽  
Adaptive Genetic Algorithm ◽  
Fitness Value ◽  
Current Engineering ◽  
Crossover And Mutation ◽  
Standard Genetic Algorithm ◽  
Self Adaptive

In order to solve the shortcomings of current engineering methods for parameters adjustment that can only deal with them according to single requirement of system and can not optimize them in the whole range, as well as the standard genetic algorithm is prone to premature convergence, therefore, an improved PID parameters adjustment method based on self-adaptive genetic algorithm was proposed. This approach enables crossover and mutation probability automatically change along with the fitness value, not only can it maintain the population diversity, but also can ensure the convergence of the algorithm. A comparison of the dynamic response between the traditional PID control and the PID control based on self-adaptive genetic algorithm was made. Simulation results show that the latter has much superiority.

Research of Image Registration Based on Maximum Entropy Template Selection Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1138-1143
Author(s):  
Hong Ying Qin
Keyword(s):  
Genetic Algorithm ◽  
Image Registration ◽  
Maximum Entropy ◽  
Evolutionary Process ◽  
Adaptive Genetic Algorithm ◽  
Image Stitching ◽  
Local Optimum ◽  
Selection Algorithm ◽  
Template Selection ◽  
Crossover And Mutation

This paper concerns an improved adaptive genetic algorithm, and the method is applied to the Maximum Entropy Template Selection Algorithm image registration. This method includes adjusting the probability of crossover and mutation in the evolutionary process. The method can overcome the disadvantage of traditional genetic algorithm that is easy to get into a local optimum answer. Results show our method is insensitive to the ordering, rotation and scale of the input images so it can be used in image stitching and retrieval of images & videos.

An Improved Genetic Algorithm for Digital Facility Layout Optimization Oriented Multi-Species and Variable-Batch Production Mode

2013 ◽  
Vol 694-697 ◽  
pp. 3632-3635
Author(s):  
Dao Guo Li ◽  
Zhao Xia Chen
Keyword(s):  
Genetic Algorithm ◽  
Facility Layout ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Batch Production ◽  
Production Mode ◽  
Slow Convergence ◽  
Weak Points

When solving facility layout problem for the digital workshop to optimize the production, the traditional genetic algorithm has its flaws with slow convergence speed and that the accuracy of the optimal solution is not ideal. This paper analyzes those weak points and proposed an improved genetic algorithm according to the characteristics of multi-species and variable-batch production mode. The proposed approach improved the convergence speed and the accuracy of the optimal solution. The presented model of GA also has been tested and verified by simulation.

Satellite Module Layout Design Based on Adaptive Bee Evolutionary Genetic Algorithm

2014 ◽  
Vol 538 ◽  
pp. 193-197
Author(s):  
Jian Jiang Su ◽  
Chao Che ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Layout Design ◽  
Adaptive Genetic Algorithm ◽  
Local Optimality ◽  
Mutation Probability ◽  
Evolutionary Genetic ◽  
Local Optimal Solution ◽  
The Moment ◽  
Crossover And Mutation

The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.

Adaptive Genetic Algorithm for Hybrid Flow-Shop Scheduling

2013 ◽  
Vol 753-755 ◽  
pp. 2925-2929
Author(s):  
Xiao Chun Zhu ◽  
Jian Feng Zhao ◽  
Mu Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Adaptive Genetic Algorithm ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
Hybrid Flow Shop Scheduling ◽  
Encoding Method ◽  
Crossover And Mutation

This paper studies the scheduling problem of Hybrid Flow Shop (HFS) under the objective of minimizing makespan. The corresponding scheduling simulation system is developed in details, which employed a new encoding method so as to guarantee the validity of chromosomes and the convenience of calculation. The corresponding crossover and mutation operators are proposed for optimum sequencing. The simulation results show that the adaptive Genetic Algorithm (GA) is an effective and efficient method for solving HFS Problems.

Optimization Design of the Crane Girder Based on Adaptive Genetic Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 123-126
Author(s):  
Peng Fei Wang ◽  
Xiu Hui Diao
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Variable ◽  
Optimization Design ◽  
Optimal Solution ◽  
Population Diversity ◽  
Main Beam ◽  
Gantry Crane ◽  
Adaptive Genetic Algorithm ◽  
Accelerate Convergence

With taking weight of single main beam of gantry crane as objective function, and taking main beam upper & lower cored, diagonal & horizontal bracing, and width & weight as design variable, this essay adopted population diversity adaptive genetic algorithm to optimize its structure and improved program design through MATLAB. This algorithm could accelerate convergence speed, which make much it easier to realize comprehensive optimal solution, since it effectively avoided weakness of basic genetic algorithm, such as partial optimal solution, prematurity and being lack of continuity, etc.

Information-Applied Technology in the Model of RBF Neural Network Based on Adaptive Genetic Algorithm for Analog Circuit Fault Diagnosis

2014 ◽  
Vol 540 ◽  
pp. 456-459
Author(s):  
Hu Cheng Zhao ◽  
Hao Lin Cui ◽  
Zhi Bin Chen
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Rbf Neural Network ◽  
Adaptive Genetic Algorithm ◽  
Connection Weight ◽  
Proposed Model ◽  
Applied Technology ◽  
Circuit Fault Diagnosis ◽  
Crossover And Mutation

To obtain the improvement of analog circuit fault diagnosis, a RBF diagnosis model based on an Adaptive Genetic Algorithm (AGA) is proposed. First an adaptive mechanism about crossover and mutation probability is introduced into the traditional genetic algorithm, and then AGA algorithm is used to optimize the network parameters such as center, width and connection weight. The experiment simulation indicates that the proposed model has exact diagnosis characteristic.

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