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.

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.

scholarly journals Optimization of construction site layout using dynamic hybrid bacterial and ant colony algorithm

2021 ◽  
Vol 15 (3) ◽  
pp. 44-54
Author(s):  
Pham Vu Hong Son
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Simulated Annealing Algorithm ◽  
Ant Colony ◽  
Construction Site ◽  
Ant Colony Optimization Algorithm ◽  
Site Layout ◽  
Dynamic Hybrid ◽  
Construction Site Layout ◽  
Parameter Values

The efficient plan of site arrangement during the construction phase has been considered a vital duty to successful project performance due to the productivity enhancement as well as safety in executions. The optimization of the Construction Site Layout Problem (CSLP) is a combinatorial complexity that regards numerous objectives and considerable growth of scale as increasing of constraints and facilities. The rearrangement on site may thus need to be had dynamic plannings at several interval schedules as construction evolves to accommodate site needs. To resolve the complexity of this problem, many studies based on the Meta-heuristic approach have been done, there are however always drawbacks and should be improved to be more optimal. This research proposes a new Hybrid Meta-heuristic model which is a combination of Ant Colony Optimization algorithm (ACO), Bacterial foraging algorithm (BFA), and Pair-Wise Exchange Heuristic algorithm (PWEH). The proposed algorithm is named Dynamic Hybrid Ant Colony Algorithm (DHACA) model that can enhance local and global searches simultaneously. In addition, parameter values are optimized to create a better solution. This research also demonstrates the effectiveness of DHACA compared with the previous studies such as Multi-objectives Genetic Algorithm (MOGA), Simulated Annealing Algorithm based Multi-objectives Genetic Algorithm (SA-based MOGA) on the CSLP. DHACA supports the construction site dynamic planning with constraints on facilities to improve work efficiency.  

scholarly journals Dynamic ant colony algorithm based on cw saving Value

2018 ◽  
Vol 246 ◽  
pp. 03015
Author(s):  
Jiang-Gu Yao ◽  
Jian Gao
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Solution Quality ◽  
Combinatorial Optimization Problems

As a swarm intelligence optimization algorithm, ant colony algorithm (ACO) has a good application in combinatorial optimization problems, in which traveling salesman problem (TSP) is an important application of ACO algorithm. It shows the powerful ability of ant colony algorithm to find short paths through graphics. However, there are obvious defects in the ant colony algorithm. When the scale of the ant colony is large, the convergence time of the algorithm becomes longer and the local optimal state is easy to fall into. In this paper, a dynamic pheromone ant colony optimization algorithm based on CW saving algorithm is proposed. Initially, a general path range is found by CW saving value algorithm, and the pheromone matrix can be reasonably configured, so that the ant colony algorithm can quickly get a better solution in the initial optimization. At the same time, the optimization scheme can be adjusted in real time according to the situation of path optimization. Large ant colony searches for other paths. Combined with 3-opt local search algorithm, the ant colony can find the optimal path more quickly. The experimental results show that the improved ant colony algorithm has better convergence speed and solution quality than other ant colony algorithms.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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.

scholarly journals Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 11-19
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Detection System ◽  
Ant Colony ◽  
Detection Accuracy ◽  
Ant Colony Optimization Algorithm ◽  
Network Intrusion ◽  
Secure Network ◽  
Main Component

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

Optimal Coordination of PSS and SSSC Controllers in Power System Using Ant Colony Optimization Algorithm

2021 ◽  
Author(s):  
Mehrdad Ahmadi Kamarposhti ◽  
Ilhami Colak ◽  
Celestine Iwendi ◽  
Shahab S. Band ◽  
Ebuka Ibeke
Keyword(s):  
Power Plant ◽  
Power System ◽  
Ant Colony Algorithm ◽  
Phase Error ◽  
Low Frequency ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Power System Stabilizers ◽  
Three Phase ◽  
Optimal Coordination

Volatility leads to disruption in synchronism between generators of a continuous system. The frequency of the volatility is usually between a few tenths of Hz to several Hz. This volatility is sometimes divided into two types, local and interregional. Local volatility is the low-frequency volatility of a power plant unit or units of a power plant relative to the grid whereas interregional volatility is the volatility of the units of one area relative to the units of another area. The worst kind of low-frequency volatility occurs when the power system in a region has a short three-phase connection to the earth, creating a complete instability of the grid and operating protective systems. One of the ways to improve the dynamic stability and steady-state of the power system is to use power system stabilizers and FACTS devices in the system. In this paper, the stabilization of the power system stabilizers (PSSs) and SSSC is done using the ant colony algorithm. Studies on a four-machine system with the three-phase error were performed in two scenarios and finally compared with the PSO method. The simulation results show that the proposed method produced more accurate performance.

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

2021 ◽  
Author(s):  
Yueping Chen ◽  
Naiqi Shang
Keyword(s):  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Path Optimization ◽  
Inspection Time ◽  
Free Form ◽  
Coordinate Measuring Machines ◽  
Ant Colony Optimization Algorithm ◽  
Measuring Machine ◽  
Free Form Surfaces

Abstract Coordinate measuring machines (CMMs) play an important role in modern manufacturing and inspection technologies. However, the inspection process of a CMM is recognized as time-consuming work. The low efficiency of coordinate measuring machines has given rise to new inspection strategies and methods, including path optimization. This study describes the optimization of an inspection path on free-form surfaces using three different algorithms: an ant colony optimization algorithm, a genetic algorithm, and a particle swarm optimization algorithm. The optimized sequence of sampling points is obtained in MATLAB R2020b software and tested on a Leitz Reference HP Bridge Type Coordinate Measuring Machine produced by HEXAGON. This study compares the performance of the three algorithms in theoretical and practical conditions. The results demonstrate that the use of the three algorithms can result in a collision-free path being found automatically and reduce the inspection time. However, owing to the different optimization methodologies, the optimized processes and optimized times of the three algorithms, as well as the optimized paths, are different. The results indicate that the ant colony algorithm has better performance for the path optimization of free-form surfaces.

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