Application of Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1217-1220
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pheromone Concentration ◽  
Local Optimal Solution ◽  
Searching Ability ◽  
Excessive Accumulation

This paper mainly considers the application of the ant colony in our life. The principle of ant colony optimization, improves the performance of ant colony algorithm, and the global searching ability of the algorithm. We introduce a new adaptive factor in order to avoid falling into local optimal solution. With the increase the number of interations, this factor will benefit the ant search the edge with lower pheromone concentration and avoid the excessive accumulation of pheromone.

scholarly journals Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Li ◽  
Hua Zhu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Parameter Selection ◽  
Ant Colony ◽  
Multidimensional Space ◽  
Bacterial Foraging ◽  
Operation Speed ◽  
Local Optimal Solution ◽  
Bacterial Foraging Algorithm ◽  
Selection For

The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA) and a particle swarm optimization (PSO), and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.

An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

2013 ◽  
Vol 389 ◽  
pp. 849-853
Author(s):  
Fang Song Cui ◽  
Wei Feng ◽  
Da Zhi Pan ◽  
Guo Zhong Cheng ◽  
Shuang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Volatility Model ◽  
Local Optimal Solution ◽  
The Impact ◽  
Improved Algorithm

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.

A Image Segmentation Method of Improved Ant Colony Algorithm for the Manipulator Self-Recognition Target

2011 ◽  
Vol 135-136 ◽  
pp. 50-55
Author(s):  
Yuan Bin Hou ◽  
Yang Meng ◽  
Jin Bo Mao
Keyword(s):  
Image Segmentation ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Immune Algorithm ◽  
Ant Colony ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Self Recognition ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm

According to the requirements of efficient image segmentation for the manipulator self-recognition target, a method of image segmentation based on improved ant colony algorithm is proposed in the paper. In order to avoid segmentation errors by local optimal solution and the stagnation of convergence, ant colony algorithm combined with immune algorithm are taken to traversing the whole image, which uses pheromone as standard. Further, immunization selection through vaccination optimizes the heuristic information, then it improves the efficiency of ergodic process, and shortens the time of segmentation effectively. Simulation and experimental of image segmentation result shows that this algorithm can get better effect than generic ant colony algorithm, at the same condition, segmentation time is shortened by 6.8%.

An Improved Ant-Colony Algorithm Based on Cloud Model Theory

2010 ◽  
Vol 143-144 ◽  
pp. 1204-1206
Author(s):  
Xian Min Wei
Keyword(s):  
Model Theory ◽  
Ant Colony Algorithm ◽  
Cloud Model ◽  
Optimal Solution ◽  
Optimal Performance ◽  
Experimental Results ◽  
Ant Colony ◽  
Local Optimal Solution ◽  
Cloud Model Theory ◽  
Improved Ant Colony Algorithm

This paper studies one method of cloud model to effectively limit the using Ant-colony Algorithm into local optimal solution, and experimental results show that this Ant-colony Algorithm can improve the speed of global search and optimal performance significantly.

scholarly journals AGV optimal path planning based on improved ant colony algorithm

2018 ◽  
Vol 232 ◽  
pp. 03052 ◽  
Author(s):  
Chengwei He ◽  
Jian Mao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Undirected Network ◽  
Optimal Path Planning ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Improved Ant Colony Algorithm

Using the traditional Ant Colony Algorithm for AGV path planning is easy to fall into the local optimal solution and lacking the capability of obstacle avoidance in the complex storage environment. In this paper, by constructing the MAKLINK undirected network routes and the heuristic function is optimized in the Ant Colony Algorithm, then the AGV path reaches the global optimal path and has the ability to avoid obstacles. According to research, the improved Ant Colony Algorithm proposed in this paper is superior to the traditional Ant Colony Algorithm in terms of convergence speed and the distance of optimal path planning.

Application of Improved Ant Colony Optimization on Economical Operation of Automatic Generation Control Units in Hydropower Station

2013 ◽  
Vol 860-863 ◽  
pp. 2101-2106 ◽  
Author(s):  
Yi Fan Li ◽  
Ke Guan Wang ◽  
Chuan Li Gong
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Automatic Generation ◽  
Ant Colony ◽  
Automatic Generation Control ◽  
Hydropower Station ◽  
Control Units ◽  
Convergence Performance ◽  
Generation Control

This paper proposed an improved ant colony optimization(ACO), to solve the economical operating dispatch of automatic generation control(AGC) units in hydropower station. The improved ant colony algorithm PSO-ACO imported particle swarm optimization is put forward. Both of the global convergence performance and the effectiveness of this algorithm is improved by using self-adaptive parameters and importing PSO to optimize the current ant paths. The mathematical description and procedure of the PSO-ACO are given with the maximum plant generating efficiency model as an example. Finally the superiority of the PSO-ACO is demonstrated by the application of AGC units on right bank of Three Gorges hydropower station. The optimal solution is more accurate and the calculation speed is higher than other methods.

scholarly journals Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

2022 ◽  
Vol 355 ◽  
pp. 03002
Author(s):  
Hongchao Zhao ◽  
Jianzhong Zhao
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Ant Colony ◽  
Local Optimal Solution ◽  
Search Speed ◽  
Aerial Vehicle ◽  
Improved Ant Colony Algorithm

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.

scholarly journals Tuning PID Controller Using Multiobjective Ant Colony Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ibtissem Chiha ◽  
Noureddine Liouane ◽  
Pierre Borne
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pid Controllers ◽  
Tuning Method ◽  
Classic Approach ◽  
Optimum Solution ◽  
Multiobjective Ant Colony Optimization

This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp,Ki, andKd) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the “Ziegler-Nichols” method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

Optimal Path Selection for Mine Emergency Rescue Based on Improved Ant Colony Algorithm

2010 ◽  
Vol 159 ◽  
pp. 168-171
Author(s):  
Feng Tian ◽  
Zhong Zhao Chen
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Lower Solution ◽  
Path Selection ◽  
Ant Colony ◽  
Emergency Rescue ◽  
Local Optimal Solution ◽  
Solution Accuracy ◽  
Improved Ant Colony Algorithm

It is the primary task to ensure the safety of lives and property of underground workers with the increasing amount of coal mining. Under the actual complex environment of coal, all kinds of uncertian factors should be considered except for the distance for the selection of the optimal path to reduce casualties. Aiming at the defect of the lower solution accuracy and tending to fall into local optimal solution of the basic ant colony algorithm(ACA), an improved ant colony algorithm is presented based on the model of ACA. Experiment results show that the new algorithm can get better results.

Research of In-Car Navigation Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 461-465
Author(s):  
Fang Ding ◽  
Su Zhuo Wu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony System ◽  
Navigation Systems ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm ◽  
Car Navigation

Determining how to select path efficiently in complex transportation networks was one of the main problems in-car navigation systems. For the drawbacks of slow convergence and easy to fall into local optimal solution of basic ant colony algorithm in solving the optimal path problem, a method of improving the expect-heuristic function is proposed in this paper, which enhances search direction and improves the convergence rate. Meanwhile, with the introduction of a new strategy to update the pheromone on ant colony system, the contradiction that convergence speed brings stagnation is balanced. The results show that the improved ant colony algorithm is easier to get the optimal solution compared with basic ant colony algorithm, and the convergence speed is faster, having a good navigation effect.

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