Research on Logistic Path Planning Strategy Based on Coarse-Grained Parallel Genetic Algorithm

2013 ◽  
Vol 798-799 ◽  
pp. 920-923
Author(s):  
Wu Xue Jiang ◽  
Xuan Zi Hu ◽  
Min Xia Liu ◽  
Peng Fei Yin
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Convergence Rate ◽  
Route Planning ◽  
Planning System ◽  
Coarse Grained ◽  
Parallel Genetic Algorithm ◽  
Planning Strategy ◽  
Elitist Strategy ◽  
Implementation Steps

In order to minimize the precocity and deceit happened in genetic algorithm, this thesis puts forward an improved intelligent evolutionary algorithmcoarse-grained parallel genetic algorithm which proposes schema order based cross operator, task immigration based cross operator, and improved approach for elitist strategy. Also, this paper applies the algorithm into logistic route planning system to clarify the concrete implementation steps for coding, population generation and genetic operator. According to experiment result, the improved algorithm mentioned in this assay have advanced the convergence rate and optimizing ability to some extent.

scholarly journals The application of Coarse-Grained Parallel Genetic Algorithm with Hadoop in University Intelligent Course-Timetabling System

2015 ◽  
Vol 10 (8) ◽  
pp. 11
Author(s):  
Liping Wu
Keyword(s):  
Genetic Algorithm ◽  
Educational Administration ◽  
Coarse Grained ◽  
Map Reduce ◽  
Parallel Genetic Algorithm ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course

The university course-timetabling problem is a NP-C problem. The traditional method of arranging course is inefficient, causes a high conflict rate of teacher resource or classroom resource, and is poor satisfaction in students. So it does not meet the requirements of modern university educational administration management. However, parallel genetic algorithm (PGA) not only have the advantages of the traditional genetic algorithm(GA), but also take full advantage of the computing power of parallel computing. It can improve the quality and speed of solving effectively, and have a broad application prospect in solving the problem of university course-timetabling problem. In this paper, based on the cloud computing platform of Hadoop, an improved method of fusing coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is deeply researched, and which is used to solve the problem of university intelligent courses arrangement. The simulation experiment results show that, compared with the traditional genetic algorithm, the coarse-grained parallel genetic algorithm not only improves the efficiency of the course arrangement and the success rate of the course, but also reduces the conflict rate of the course. At the same time, this research makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm, and also solves the problem of university scheduling problem more effectively.

scholarly journals A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi Chen ◽  
Tao Lin ◽  
Ningjiu Tang ◽  
Xin Xia
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Coarse Grained ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Subset ◽  
Parallel Genetic Algorithm ◽  
Support Vectors ◽  
Optimal Feature Subset ◽  
Optimal Feature

The extensive applications of support vector machines (SVMs) require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA) is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA) based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.

A GPU-Enabled Parallel Genetic Algorithm for Path Planning of Robotic Operators

2014 ◽  
pp. 1-13 ◽  
Author(s):  
Panpan Cai ◽  
Yiyu Cai ◽  
Indhumathi Chandrasekaran ◽  
Jianmin Zheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Parallel Genetic Algorithm

An Adaptive Genetic Algorithm Based on Multi-Population Parallel Evolutionary for Highway Alignment Optimization Model

2011 ◽  
Vol 58-60 ◽  
pp. 1499-1503 ◽  
Author(s):  
Jian Xin Chen ◽  
Yong Yi Guo ◽  
Mai Xia Lv
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Single Species ◽  
Adaptive Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Single Population ◽  
Highway Design ◽  
Population Evolution ◽  
Highway Alignment Optimization ◽  
Highway Alignment

Based on the characteristics of the highway design, this paper transfers all the factors involved in the highway design to a cost-optimized-oriented model and designs a variety parallel genetic algorithm to optimize highway design. While maintaining evolution stability of excellent individual, the algorithm can improve convergence rate and accuracy and avoid premature convergence generated by single-population evolution. To some extent, it makes up generalization-lacking defects of a single species or steady parameters in premature overcoming. Finally, the algorithm is verified with a good result. This algorithm provides a useful method for highway design.

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