scholarly journals Ant Colony Optimization Algorithm Model Based on the Continuous Space

2016 ◽  
Vol 12 (12) ◽  
pp. 27 ◽  
Author(s):  
Xuepeng Huang
Keyword(s):  
Ant Colony Algorithm ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Optimization Strategy ◽  
Ant Colony Optimization Algorithm ◽  
Continuous Space ◽  
Distributed Parallel Computing ◽  
Branching Factor ◽  
Positive Feedback Mechanism ◽  
Distribution Algorithms

Ant colony algorithm is a heuristic algorithm which is　fit for solving complicated combination optimization.It showed great advantage on solving combinatorial optimization problem since it was proposed. The algorithm uses distributed parallel computing and positive feedback mechanism, and is easy to combine with other algorithms.This ant colony algorithm has already been widespread used in the field of discrete space optimization, however, is has been rarely used for continuous space optimization question.On the basis of basic ant colony algorithm principles and mathematical model, this paper proposes an ant colony algorithm for solving continuous space optimization question.Comparing with the ant colony algorithm, the new algorithm improves the algorithm in aspects of ant colony initialization, information density function, distribution algorithms, direction of ant colonymotion, and so on. The new algorithm uses multiple optimization strategy, such as polynomial time reduction and branching factor, and improves the ant colony algorithm effectively.

Study of Sintering Blending Based on Swarm Intelligence Optimization Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1550-1553 ◽  
Author(s):  
Hui Zhao ◽  
Ming Wang ◽  
Hong Jun Wang ◽  
You Jun Yue
Keyword(s):  
Exact Solution ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Positive Feedback Mechanism ◽  
Swarm Intelligence Optimization

The sintering blending is a complex nonlinear optimization problem. The traditional single algorithm can not meet the requirement of good quality of sinter and lowest costs well. So, a hybrid optimization method of particle swarm and ant colony algorithm was proposed. The method gives full play to the global convergence of particle swarm optimization algorithm, takes it as a preliminary search, then use the positive feedback mechanism of ant colony algorithm for the exact solution, to make these two algorithms to reach a complementary, in order to get a rapid exact solution. The simulation results show that the proposed hybrid algorithm has fast convergence and high accuracy, which can effectively reduce the sintering cost.

An Improved Ant Colony Algorithm Applied in the TSP Problem

2013 ◽  
Vol 380-384 ◽  
pp. 1738-1741
Author(s):  
Meng Lan Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Group Behavior ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Heuristic Information ◽  
Random Probability ◽  
Positive Feedback Mechanism ◽  
Maximum Minimum ◽  
Improved Ant Colony Algorithm

Ant colony algorithm is a kind of intelligent algorithm imitating the group behavior of ants. The positive feedback mechanism is not only its advantage which makes the ant colony algorithm quickly converge to optimal solutions of a problem, but also its defect which makes it easy to fall into the local optimal solutions. ACS and MMAS are the two typically improved ant algorithms by introducing the pseudo random probability selection rule and maximum-minimum pheromone restriction rule to accelerate the converging speed of this algorithm and avoid falling into local optimal solutions. At present, there is no algorithm put forward to improve the algorithm using the effect of the heuristic information. This paper presents an improved ant colony algorithm based on the heuristic information of direction, and provides a new idea for the study on the improved ant colony algorithm.

scholarly journals The Improved Ant Colony Optimization Algorithm for MLP considering the Advantage from Relationship

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yabo Luo ◽  
Yongo P. Waden
Keyword(s):  
Ant Colony Optimization ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Rectangular Coordinate System ◽  
Design Variables ◽  
Pheromone Concentration ◽  
Machine Layout ◽  
Polar Coordinate ◽  
Positive Feedback Mechanism

An improved ant colony optimization (ACO) is presented to solve the machine layout problem (MLP), and the concept is categorized as follows: firstly, an ideology on “advantage from quantity” and “advantage from relationship” is proposed and an example is demonstrated. In addition, the strategy of attached variables under local polar coordinate systems is employed to maintain search efficiency, that is, “advantage from relationship”; thus, a mathematical model is formulated under a single rectangular coordinate system in which the relative distance and azimuth between machines are taken as attached design variables. Further, the aforementioned strategies are adopted into the ant colony optimization (ACO) algorithm, thereby employing the inverse feedback mechanism for dissemination of pheromone and the positive feedback mechanism for pheromone concentration. Finally, the effectiveness of the proposed improved ACO is tested through comparative experiments, in which the results have shown both the reliability of convergence and the improvement in optimization degree of solutions.

Ant Colony Algorithm Dynamically Adjust the Parameters Based on Chaos Theory

2014 ◽  
Vol 513-517 ◽  
pp. 1787-1792
Author(s):  
Tao Gui ◽  
Xue Liang Fu ◽  
Gai Fang Dong ◽  
Xun Ying Sun ◽  
Li Min Bao
Keyword(s):  
Chaos Theory ◽  
Ant Colony Algorithm ◽  
Algorithm Design ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Dynamic Parameters ◽  
Dynamic Algorithm ◽  
Positive Feedback Mechanism ◽  
Global Parameters ◽  
Search Capability

Ant colony algorithm as an intelligent bionic optimization algorithm, Because of its use of positive feedback mechanism, the result will be prone to premature, stagnation and slow speed of solving the problem etc. For this shortcoming is proposed based on chaos theory adaptive dynamic parameters ant colony algorithm (PDSACA Dynamic Parameters Self-adaptive Ant Colony Algorithm).In the process of the dynamic algorithm solving, introducing chaotic disturbance technique, the parameters of the algorithm design of dynamic changes to affect the algorithm quality and global parameters are adjusted adaptively to improve the global search capability. By using the TSPLABs reference example to test the algorithm. Experimental results show that the convergence of the algorithm, robustness and efficiency have been improved to Compare with the basic ant colony algorithm.

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.

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.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

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