Study on Traveling Salesman Problem Based on the Improved Chaos Ant Colony Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2196-2199 ◽  
Author(s):  
Hai Yang
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Elite Strategy ◽  
Novel Method ◽  
Simulation Results ◽  
Improved Algorithm

In this paper an improved chaos ant colony algorithm based on return optimization strategy, elite strategy and intersection removal strategy is proposed. The improved algorithm uses orthogonal method to cluster the target points, then adopt chaos technology to optimize initial solution of the ant colony to improve individual quality and chaos perturbation is utilized to avoid the search being trapped into local optimum solutions. The simulation results show that the improved algorithm has higher efficiency in finding optimal path and it is a novel method to solve traveling salesmen problem.

Cutting trajectory planning of sections with complex composition for roadheader

2018 ◽  
Vol 233 (4) ◽  
pp. 1441-1452 ◽  
Author(s):  
Suyu Wang ◽  
Miao Wu
Keyword(s):  
Trajectory Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Complex Composition ◽  
Cutting Head ◽  
Pheromone Concentration ◽  
Simulation Results

In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

A Novel Collaborative Optimization Algorithm for Solving TSP

2014 ◽  
Vol 543-547 ◽  
pp. 1795-1798
Author(s):  
Di Zhou
Keyword(s):  
Ant Colony Algorithm ◽  
Pso Algorithm ◽  
Collaborative Optimization ◽  
Ant Colony ◽  
Optimization Strategy ◽  
Distribution Problem ◽  
Chaos Optimization ◽  
Exchange Operation ◽  
Optimal Values ◽  
Simulation Results

For the premature convergence and initial pheromone distribution problem of the basic ACO algorithm, PSO algorithm and chaos optimizing strategy are introduced into the ant colony algorithm in order to propose a novel collaborative optimization (CPACO) algorithm based on the collaboration theory. The first, the CPACO algorithm divides the ant colony into several subgroups, and the parameters of the subgroup are regarded as the particles. Then these advantages of PSO algorithm and chaos optimization strategy are fully utilized to optimize these parameters of the ACO algorithm in order to obtain the optimal values of these parameters. And the pheromone exchange operation is introduced into the subgroup. In order to validate the performance of the CPACO algorithm, the TSP problems are selected in here. The simulation results show that the proposed CPACO algorithm has better optimization performance than the traditional ACO algorithm.

Path Planning of Robot Based on Ant Colony Optimization Algorithm

2014 ◽  
Vol 614 ◽  
pp. 199-202 ◽  
Author(s):  
Bao Ming Shan ◽  
De Xiang Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Robot Path Planning ◽  
Ant Colony Optimization Algorithm ◽  
Simulation Results ◽  
Robot Path

This paper presents a method for robot path planning based on ant colony optimization algorithm, in order to resolve the weakness of ant colony algorithm such as slow convergence rate and easy to fall into local optimum and traps. This method uses anti-potential field to make the robot escape from them smoothly, and at the end of each cycle, uses the way of judge first and then hybridization to optimize the algorithm. Finally, the simulation results show that the performance of the algorithm has been improved, and proved that the optimization algorithm is valid and feasible.

Path Planning for Mobile Robot Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 717-720 ◽  
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Probability Function ◽  
Ant Colony ◽  
Local Optimum ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Improved Ant Colony Algorithm ◽  
Improved Algorithm

This paper presents a dynamic path planning method based on improved ant colony algorithm. In order to increasing the algorithm’s convergence speed and avoiding to fall into local optimum, we propose adaptive migratory probability function and updating the pheromone. We apply the improved algorithm to path planning for mobile robot and the simulation experiment proved that improved algorithm is viable and efficient.

Using Cellular Ant Colony Algorithm for Path-Planning of Robots

2012 ◽  
Vol 182-183 ◽  
pp. 1776-1780 ◽  
Author(s):  
Yong Fen Wu ◽  
Xin Xing Zhang ◽  
Jun Qing Wu
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colonies ◽  
Ant Colony Algorithms ◽  
Current Region ◽  
Novel Method ◽  
Short Time ◽  
Robot Path

To overcome some shortcoming existed in the conventional ant colony algorithms, e.g. slow converging and trend for falling into local convergences, a novel method for robot path planning is introduced based on cellular ant colony. Firstly, two ant colonies were set to run with different strategies. Secondly, the existing ant colony paths were evolved by following the cellular rules, so that the ants could jump from the current region into the region with a solution. Experiment results showed that the proposed algorithm proved to be stable, and that the global optimal path was found in a short time in a number of iterations.

A Dynamic Edge Exchanged Ant Colony Algorithm for TSP Problem

2014 ◽  
Vol 687-691 ◽  
pp. 1608-1611
Author(s):  
Yu Zhong Liu ◽  
Hua Ping Yu
Keyword(s):  
Ant Colony Algorithm ◽  
Heuristic Algorithms ◽  
Local Optimization ◽  
Ant Colony ◽  
Premature Convergence ◽  
Local Optimum ◽  
Dynamic Adjustment ◽  
Low Speed ◽  
Simulation Results ◽  
Dynamic Exchange

Aimed at solving premature convergence and low speed in heuristic algorithms for TSP problems, this paper analyzed the principle of Max-Min Ant colony algorithm (MMAS) and Lin-Kernighan algorithm, then proposed a dynamic exchange of Max-Min Ant colony algorithm (MMAS-LK). The new algorithm used MMAS to initially a set of the solutions in the early state, then utilized the improved Lin-Kernighan algorithm for local optimization, and dynamic adjustment parameters according to the process of computing avoid falling into local optimum. The simulation results showed that the proposed algorithm compared with the original MMAS and Lin-Kernighan algorithm, it has a better speed and precision in the TSP problem.

scholarly journals Research on path planning of cleaning robot based on an improved ant colony algorithm

2021 ◽  
Vol 336 ◽  
pp. 07005
Author(s):  
Zhidong Wang ◽  
Changhong Wu ◽  
Jing Xu ◽  
Hongjie Ling
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Cleaning Robot ◽  
Initial Stage ◽  
Searching Ability ◽  
Improved Ant Colony Algorithm

The conventional ant colony algorithm is easy to fall into the local optimal in some complex environments, and the blindness in the initial stage of search leads to long searching time and slow convergence. In order to solve these problems, this paper proposes an improved ant colony algorithm and applies it to the path planning of cleaning robot. The algorithm model of the environmental map is established according to the grid method. And it built the obstacle matrix for the expansion and treatment of obstacles, so that the robot can avoid collision with obstacles as much as possible in the process of movement. The directional factor is introduced in the new heuristic function, and we can reduce the value of the inflection point of paths, enhance the algorithm precision, and avoid falling into the local optimal. The volatile factor of pheromones with an adaptive adjustment and the improved updating rule of pheromones can not only solve the problem that the algorithm falls into local optimum, but also accelerate the running efficiency of the algorithm in the later stage. Simulation results show that the algorithm has the better global searching ability, the convergence speed is obviously accelerated, and an optimal path can be planned in the complex environment.

Research on BP Neural Network Optimization Based on Ant Colony Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1819-1821 ◽  
Author(s):  
Rui Wang ◽  
Na Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Initial Value ◽  
Neural Network Optimization ◽  
Optimal Weights

This paper presents an ant colony algorithm and BP algorithm together to complete the learning algorithm for neural networks ACO-BP algorithm. The algorithm adopts the ant colony algorithm for global optimization of the network weights, overcome the disadvantage of BP algorithm that is easy to fall into local optimum; then, the optimal weights found by BP algorithm as the initial value, further optimization. Finally, the simulation experiments show that, if the network structure is determined by the condition, this algorithm not only speeds up the convergence speed of the improved ant colony algorithm of optimal solution, but also can avoid falling into local optimal path. It will increase the reliability.

scholarly journals Spectral line extraction based on ant-colony algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yusheng Cheng ◽  
Kai Ma ◽  
Haitao Li ◽  
Shilin Sun ◽  
Yichuan Wang
Keyword(s):  
Spectral Line ◽  
Ant Colony Algorithm ◽  
Signal To Noise Ratio ◽  
Optimal Path ◽  
Spectral Lines ◽  
Ant Colony ◽  
Optimization Strategy ◽  
Signal To Noise ◽  
Line Extraction ◽  
Noise Ratio

AbstractA spectral-line-extraction algorithm based on the ant-colony algorithm is proposed to address the difficulty of extracting spectral lines in low signal-to-noise ratio conditions, and the problem that results from the optimal path algorithm falls into local optimization. The algorithm applies the ant-colony path-optimization strategy to detect a spectral line and constructs a corresponding mathematical model using the grid method. A new cost function is proposed to replace path length as the optimization standard in the conventional ant-colony algorithm. At the same time, the roulette rule is used to determine the direction of the next step. This algorithm improves the traditional heuristic function, increases the attraction of the target spectral line to the route search, and improves the convergence rate. Sea-trial data show that the algorithm performs better in extracting spectral lines with a low signal-to-noise ratio than the optimal path algorithm.

Path Planning and Replanning for Intelligent Robot Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 390 ◽  
pp. 495-499 ◽  
Author(s):  
Bi Wei Tang ◽  
Zhan Xia Zhu ◽  
Qun Fang ◽  
Wei Hua Ma
Keyword(s):  
Path Planning ◽  
Objective Function ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Intelligent Robot ◽  
Intelligent Robots ◽  
Simulation Results ◽  
Improved Ant Colony Algorithm ◽  
Self Adaptive

The effectiveness of path planning and path replanning for intelligent robot using improved ant colony algorithm is explored in this paper. For the purpose of avoiding falling into local optimum and preventing iterative stagnant, this paper describes a new algorithm named stochastic self-adaptive ant colony algorithm to improve the basic ant colony algorithm. Based on the improved ant colony algorithm, the approaches of path planning and path replanning are presented in this paper. Aiming at improving the speed of the algorithm and simplifying the objective function of traditional path planning, this paper presents a principle of eliminating the path nodes .Finally, some constrast emulators are designed.The simulation results proves that the improved ant colony algorithm has strong adaptability in intelligent robots path path planning and replanning.

Sign in / Sign up

Export Citation Format

Share Document