scholarly journals Genetic algorithm for optimization in adaptive bus signal priority control

2012 ◽  
Vol 3 (1) ◽  
pp. 35-43
Author(s):  
Tran Vu TU ◽  
Kazushi SANO
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Computation Time ◽  
Signalized Intersections ◽  
Convergence Speed ◽  
Rate Performance ◽  
Improved Genetic Algorithm ◽  
Slow Convergence ◽  
Bus Signal Priority ◽  
Compensation Rule

This paper firstly proposes an improved genetic algorithm (GA) for optimization in adaptive bus signal priority control at signalized intersections. Unlike conventional genetic algorithms with slow convergence speed, this algorithm can increase the convergence speed by utilizing the compensation rule between consecutive signal cycles to narrow new possible generated population spaces. Secondly, the paper would like to present a way to apply the algorithm to a simple adaptive bus signal priority control as well as compare how much the computation time is saved when applying the improved algorithm. Then the research thirdly investigates the efficiency of the proposed algorithm under various flow rate situations. The results show that the improved genetic algorithm can reduce the computation time considerably, by up to 48.39% for the studied case.  With high saturation degrees on the cross street, the convergence rate performance of the improved genetic algorithm is significantly good. The figure can be up to 36.2% when compared with the convergence rate of the conventional GA.

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.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

Solving Job-Shop Scheduling Problem by an Improved Genetic Algorithm

2011 ◽  
Vol 411 ◽  
pp. 588-591
Author(s):  
Yan Li Yang ◽  
Wei Wei Ke
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Local Optimum ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Global Optimal ◽  
Simulation Results

An improved genetic algorithm is proposed by introducing selection operation and crossover operation, which overcomes the limitations of the traditional genetic algorithm, avoids the local optimum, improves its convergence rate and the diversity of population, and solves the problems of population prematurity and slow convergence rate in the basic genetic algorithm. Simulation results show that compared with other improved genetic algorithms, the proposed algorithm is better in finding global optimal and convergent rate.

Review of Task Scheduling Algorithm Research in Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 3236-3239 ◽  
Author(s):  
Mei Geng Huang ◽  
Zhi Qi Ou
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Slow Convergence

The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

The Application of Improved Genetic Algorithm in Distribution Network Reconfiguration

2012 ◽  
Vol 490-495 ◽  
pp. 1689-1693 ◽  
Author(s):  
Wen Hua Zhou ◽  
Xiao Long Chen
Keyword(s):  
Genetic Algorithm ◽  
Distribution Network ◽  
Estimation Method ◽  
Computation Time ◽  
Original Data ◽  
Engineering Practice ◽  
Network Reconfiguration ◽  
Improved Genetic Algorithm ◽  
Load Curve ◽  
Distribution Network Reconfiguration

This paper presents an improved algorithm for distribution network reconfiguration. The objectives is to minimized the power loss and the percentage of over-voltage. Based on the traditional genetic algorithm, the adaptable function selection and the disposal of terminating evolution criteria has been improved, to improve the convergence of the system and the calculation accuracy. At the same time, using a new estimation method to correct the load curve. This approach takes full advantage of existing distribution network's original data, it can significantly reduce the computation time, its accuracy to meet the requirements of engineering practice. Test results have been presented along with the discussion of the algorithm.

An Improved Genetic Algorithm Based on Cellular Automata

2013 ◽  
Vol 340 ◽  
pp. 727-731
Author(s):  
Hong Tang ◽  
Yun Sheng Ge ◽  
Xiao Hai Pan ◽  
Shu Feng We
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Search Time ◽  
Target Function ◽  
Convergence Speed ◽  
Optimal Result ◽  
Improved Genetic Algorithm ◽  
Simple Genetic Algorithm

In order to overcome the drawbacks of Simple Genetic Algorithm such as cannot get the most optimal result, low convergence speed et al. Cellular Simple Genetic Algorithm-a new genetic algorithm based on Cellular Automata-is presented in this paper. Compared with the Simple Genetic Algorithm, the experiment results show the Cellular Simple Genetic Algorithm has remarkable advantages in following aspects: reducing the search-time and improving the precise of target function.

scholarly journals Comparative Analysis of Distributive Optimized Clustering Techniques in Cognitive Radio Networks

2018 ◽  
Vol 7 (3.27) ◽  
pp. 504 ◽  
Author(s):  
R Ganesh Babu ◽  
Dr V.Amudha
Keyword(s):  
Genetic Algorithm ◽  
Comparative Analysis ◽  
Cognitive Radio ◽  
Convergence Rate ◽  
Optimization Algorithm ◽  
Computation Time ◽  
Radio Networks ◽  
Optimization Techniques ◽  
Dynamic Clustering ◽  
Clustering Techniques

In this paper we study and compare the performance of Distributed Firefly Optimized Clustering (DFOC) with Distributed Swarm Optimized Clustering (DSOC) optimization techniques used for the dynamic clustering. Proposed Distributed Firefly Optimized Clustering (DFOC) is an optimization algorithm  based on the function of attractiveness of firefly behavior. All the cognitive nodes move towards the brighter firefly with random velocity to form an organized cluster with least computation time. In the existing DSOC method each particle’s best position and velocity are evaluated according to the objective function until an optimum global best position is reached. The convergence rate of DSOC is similar to Genetic Algorithm (GA). The proposed DFOC, the SU power is reduced to 7.34% for 100 numbers of SUs.compared to DSOC.  

The Application of the Improved Genetic Algorithm in the Aeroengine Fault Diagnosis

2013 ◽  
Vol 846-847 ◽  
pp. 840-843
Author(s):  
Xiao Bo Liu ◽  
Jun Chao Tu ◽  
Liang Ni Shen
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Fitness Function ◽  
Convergence Speed ◽  
Fault Classification ◽  
Fault Identification ◽  
Improved Genetic Algorithm ◽  
Characteristic Parameters

A improved genetic algorithm is proposed based on a new fitness function in allusion to the problem that the traditional genetic algorithm is not fully consider the knowledge of the problem itself.The improved genetic algorithm is used to analyze the fault feature , to extract the fault and remove redundant characteristic parameters for the fault classification and calculation.The diagnosis example shows that the method has faster convergence speed and can be effective for fault identification.

Immune Programming Applications in Image Segmentation

2011 ◽  
pp. 304-321
Author(s):  
Xiaojun Bi
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Theoretical Analysis ◽  
Evolutionary Algorithm ◽  
Optimization Algorithms ◽  
Convergence Speed ◽  
Experimental Results ◽  
Intelligent Algorithms ◽  
Slow Convergence ◽  
Immune Programming

In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete the task of image segmentation. Traditional evolutionary algorithm represented by Genetic Algorithm is an efficient approach for image segmentation, but in the practical application, there are many problems such as the slow convergence speed of evolutionary algorithm and premature convergence, which have greatly constrained the application. The goal of introducing immunity into the existing intelligent algorithms is to utilize some characteristics and knowledge in the pending problems for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Theoretical analysis and experimental results show that immune programming outperforms the existing optimization algorithms in global convergence speed and is conducive to alleviating the degeneration phenomenon. Theoretical analysis and experimental results show that immune programming has better global optimization and outperforms the existing optimization algorithms in alleviating the degeneration phenomenon. It is a feasible and effective method of image segmentation.

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.

Sign in / Sign up

Export Citation Format

Share Document