An Improved Two-Dimensional Entropic Thresholding Method Based on Ant Colony Genetic Algorithm

被引：2

作者：

Shen, Xiaohong ^{[1
,2
]}

Zhang, Yulin ^{[1
]}

Shen, Xiaohong ^{[1
,2
]}

Li, Fangzhen ^{[2
]}

机构：

[1] Shandong Univ, Sch Control Sci & Engn, 73 Jingshi Rd, Jinan 250100, Peoples R China

[2] Shangdong Econ Univ, Sch Comp Sci & Technol, Jinan, Peoples R China

来源：

PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL I | 2009年

基金：

中国国家自然科学基金;

关键词：

threshold; two-dimensional entropy; segmentation; genetic algorithm; ant colony optimization;

D O I：

10.1109/GCIS.2009.96

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The conventional two-dimensional (2-D) entropic thresholding is time consuming due to the exhaustive search in 2-D space. An improved 2-D entropic thresholding method based on ant colony genetic algorithm is proposed. This method extends ant colony genetic algorithm to 2-D discrete space optimization and includes the conventional 2-D entropic thresholding method. In this method, the ant is at the same time the chromosome. To reflect the collaboration of ants, the 2-D entropy of the ant as well as the pheromone is used to construct the fitness function. The best threshold vector is obtained by the genetic evolution of ant colony. Experiments show that the accuracy, stability and search efficiency of this method are better than that of the 2-D entropic algorithm based on genetic algorithm or ant colony optimization.

引用

页码：163 / +

页数：3

共 50 条

[21] Research on Vehicle Routing Problem with Time Windows Based on Improved Genetic Algorithm and Ant Colony Algorithm
Chen, Guangqiao
Gao, Jun
Chen, Daozheng
ELECTRONICS, 2025, 14 (04):
[22] A Sharing Bike scheduling Optimization Algorithm Based on Two-dimensional Dynamic Model and Improved Genetic Algorithm
Liu, Zhenheng
Ren, Lei
IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY, 2018, : 801 - 806
[23] Optimized routing method for wireless sensor networks based on improved ant colony algorithm
Khapre, Shailesh Pancham
Chopra, Suhail
Khan, Arshad
Sharma, Pavika
Shankar, Achyut
PROCEEDINGS OF THE CONFLUENCE 2020: 10TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING, 2020, : 455 - 458
[24] A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing
Liu, Chun-Yan
Zou, Cheng-Ming
Wu, Pei
PROCEEDINGS OF THIRTEENTH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE, (DCABES 2014), 2014, : 68 - 72
[25] An Optimization Method for Satellite Data Structure Design Based on Improved Ant Colony Algorithm
Zhao, Jinchen
Ye, Mian
IEEE ACCESS, 2023, 11 : 64941 - 64956
[26] Two-dimensional entropic segmentation
Jansing, ED
Albert, TA
Chenoweth, DL
PATTERN RECOGNITION LETTERS, 1999, 20 (03) : 329 - 336
[27] Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm
Shi, Kangjing
Huang, Li
Jiang, Du
Sun, Ying
Tong, Xiliang
Xie, Yuanming
Fang, Zifan
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2022, 10
[28] An Improved Ant Colony Algorithm Combined with Genetic Algorithm and Its Application in Image Segmentation
Zhou Haifeng
INTELLIGENCE COMPUTATION AND EVOLUTIONARY COMPUTATION, 2013, 180 : 389 - 393
[29] An improved feature selection algorithm based on graph clustering and ant colony optimization
Ghimatgar, Hojat
Kazemi, Kamran
Helfroush, Mohamamd Sadegh
Aarabi, Ardalan
KNOWLEDGE-BASED SYSTEMS, 2018, 159 : 270 - 285
[30] Wireless sensor network node optimal coverage based on improved genetic algorithm and binary ant colony algorithm
Jingwen Tian
Meijuan Gao
Guangshuang Ge
EURASIP Journal on Wireless Communications and Networking, 2016

← 1 2 3 4 5 →