An Optimization Approach of Ant Colony Algorithm and Adaptive Genetic Algorithm for MCM Interconnect Test

2009 ◽  
Author(s):  
Chen Lei ◽  
Quanhui Liu
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Adaptive Genetic Algorithm ◽  
Optimization Approach ◽  
Interconnect Test

Optimization of Processing Parameters in Injection Molding Based on Adaptive Ant Colony Algorithm

2011 ◽  
Vol 179-180 ◽  
pp. 304-310 ◽  
Author(s):  
Feng Li Huang ◽  
Shui Sheng Chen ◽  
Jin Mei Gu
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Kriging Model ◽  
Ant Colony ◽  
Injection Molding Process ◽  
Adaptive Genetic Algorithm ◽  
Molding Process ◽  
Correlation Degree

An optimization method, integrating correlation degree, response surface method and ant colony algorithm, is proposed for exploring optimal parameters with the molding quality evaluation of warpage amount in injection molding. Initially, a novel formula calculating correlation degree is brought forth on the basis of the definition of distance and place value, and the parameters are chosen using the correlation degree method. Then the approximate model of the injection molding process is constructed by Kriging model with the determined parameters. Finally, the adaptive genetic algorithm and the ant colony algorithm are adopted to solve the optimization problem respectively, and injection molding tests are experimentally performed to validate the optimization results of parameters in injection molding. The experimental results demonstrate that the ant colony algorithm is superior to the genetic algorithm in solving the optimization problem for the low-dimensional design variables vector and the short coding length.

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.

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