Freeway Density Control Based on the Fusion of Genetic Algorithm and Ant Colony Algorithm

2015 ◽  
Vol 12 (17) ◽  
pp. 6535-6544 ◽  
Author(s):  
Liefeng Liu
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Density Control

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

2010 ◽  
Vol 26-28 ◽  
pp. 620-624 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang ◽  
Tuan Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Research Attention ◽  
Simply Supported ◽  
Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

Simulation of English text recognition model based on ant colony algorithm and genetic algorithm

2021 ◽  
pp. 1-12
Author(s):  
Fei Long
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Text Recognition ◽  
English Text ◽  
Recognition Model ◽  
Central Node ◽  
Model Based ◽  
Characteristic Points

The difficulty of English text recognition lies in fuzzy image text classification and part-of-speech classification. Traditional models have a high error rate in English text recognition. In order to improve the effect of English text recognition, guided by machine learning ideas, this paper combines ant colony algorithm and genetic algorithm to construct an English text recognition model based on machine learning. Moreover, based on the characteristics of ant colony intelligent algorithm optimization, a method of using ant colony algorithm to solve the central node is proposed. In addition, this paper uses the ant colony algorithm to obtain the characteristic points in the study area and determine a reasonable number, and then combine the uniform grid to select some non-characteristic points as the central node of the core function, and finally use the central node with a reasonable distribution for modeling. Finally, this paper designs experiments to verify the performance of the model constructed in this paper and combines mathematical statistics to visually display the experimental results using tables and graphs. The research results show that the performance of the model constructed in this paper is good.

A Kind of Network Intrusion Detection Method Using Improved Support Vector Machine Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2995-2998
Author(s):  
Xiaoqin Zhang ◽  
Guo Jun Jia
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Ant Colony Algorithm ◽  
Small Sample ◽  
Ant Colony ◽  
Support Vector ◽  
Algorithm Selection ◽  
Data Set ◽  
Network Intrusion

Support vector machine (SVM) is suitable for the classification problem which is of small sample, nonlinear, high dimension. SVM in data preprocessing phase, often use genetic algorithm for feature extraction, although it can improve the accuracy of classification. But in feature extraction stage the weak directivity of genetic algorithm impact the time and accuracy of the classification. The ant colony algorithm is used in genetic algorithm selection stage, which is better for the data pretreatment, so as to improve the classification speed and accuracy. The experiment in the KDD99 data set shows that this method is feasible.

Low-Altitude Airship Remote Sensing Images Matching Based on Improved Hybrid Ant Colony Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 924-928
Author(s):  
Bang Long Pan ◽  
Wei Ning Yi ◽  
Xian Hua Wang
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Data Processing ◽  
Image Matching ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Registration Process ◽  
Low Altitude ◽  
Image Pairs

Low-altitude unmanned airship remote sensing is attractive to various applications. However, at present, since the airship is bulky, weak to resist wind, unstable to flight attitude, and can not be equipped with specialized remote sensing sensors, image data processing is confronted with new challenges when traditional data processing methods are used. In this paper, improved hybrid ant colony algorithm (HACA), a new image matching method, is proposed. Firstly we perform a pre-registration process that roughly aligns the image pairs by GPS/electronic compass geolocation. Once the pre-registration is completed, a fine-scale registration process is executed by applying a hybrid algorithm of genetic algorithm (GA) and ant colony algorithm (ACA) based on neighborhood search strategy that is detected by the linear quadtree Morton coding. The image pairs are then matched by using optimal solution obtained from the automatic updates of ant colony pheromone. By compared with traditional genetic algorithm and ant colony algorithm, the improved HACA results show that search calculation time is increased by the maximum 406℅, standard root mean square error of image matching by the best 235℅. The experiment result proves that improved HACA exactly provides an effective method for image matching. The local maxima of the function can be avoided efficiently and search speed of the global optimum is increased greatly.

Logistics Route Optimization Based on Improved Fish Swarm Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1761-1764
Author(s):  
Feng Kai Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Low Cost ◽  
Ant Colony ◽  
Route Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Logistics Distribution ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

In order to achieve a low cost and low exhaust pollution in logistics distribution path. In view of the shortages of existing genetic algorithm and ant colony algorithm which have the characteristics of some limitations, such as ant colony algorithm's convergence slow, easy going, the characteristics of such as genetic algorithm premature convergence in the process of path optimization, process complex, the paper proposed the improved artificial fish swarm algorithm in order to solve logistics route optimization problem. At last, through simulation experiment, the improved artificial fish swarm algorithm is proved correct and effective.

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