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.

Optimization on NoC Mapping Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 539 ◽  
pp. 280-285 ◽  
Author(s):  
Dong Li
Keyword(s):  
Power Consumption ◽  
Ant Colony Algorithm ◽  
Simulation Experiment ◽  
Local Optimization ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Optimization Scheme ◽  
Tabu List ◽  
Mapping Algorithm ◽  
Improved Ant Colony Algorithm

Traditional ant colony mapping algorithm not only has big power consumption, but also is easy to be trapped into local optimization on NoC mapping, for which the paper proposes an optimization scheme based on improved ant colony algorithm. Firstly, the parameters are for initialization operation. Secondly, tabu list is used to solve them, and the solutions are for local optimization of optimal solutions by using 2-opt algorithm. Lastly, pheromone rules are updated. Simulation experiment indicates that compared with traditional ant colony mapping algorithm, NoC mapping optimization scheme based on improved ant colony algorithm not only has better performance on mapping power consumption, but also is not easy to be trapped into local optimization.

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.

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.

Improved Ant Colony Algorithm and Application in Sequence Images of Prostate DWI Registration

2014 ◽  
Vol 1049-1050 ◽  
pp. 530-534
Author(s):  
Xiao Ping Zong ◽  
Hai Bin Zhang ◽  
Lei Hao ◽  
Pei Guang Wang
Keyword(s):  
Continuous Function ◽  
Diffusion Weighted Imaging ◽  
Ant Colony Algorithm ◽  
Transition Probabilities ◽  
Function Optimization ◽  
Experimental Results ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Ant System ◽  
Improved Ant Colony Algorithm

Because of the drift which exists in sequence image of prostate DWI (Diffusion Weighted Imaging), the global ant colony algorithm is introduced into the paper for registration optimization. The paper introduces an ant colony algorithm for continuous function optimization, based on max-min ant system (MMAS). This paper controls the transition probabilities and enhances the abilities of ants seeking globally optimal solutions by adding an adjustable factor in the basic ant colony algorithm and updating the local pheromone and global pheromone. Experimental results verify the effectiveness of the algorithm.

scholarly journals Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenming Wang ◽  
Jiangdong Zhao ◽  
Zebin Li ◽  
Ji Huang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Planning Problem ◽  
Turning Angle ◽  
Heuristic Information ◽  
Starting Point ◽  
Improved Ant Colony Algorithm

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

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.

Sign in / Sign up

Export Citation Format

Share Document