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.

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.

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.

Adaptive Ant Colony Algorithm Used in WSNs Routing for Information Acquisition of Manufacturing Process

2008 ◽  
Vol 392-394 ◽  
pp. 985-989
Author(s):  
G.T. Wang ◽  
Jing Ning ◽  
L.M. Wu
Keyword(s):  
Routing Protocol ◽  
Manufacturing Process ◽  
Information Acquisition ◽  
Ant Colony Algorithm ◽  
Shortest Paths ◽  
Weighting Factor ◽  
Ant Colony ◽  
Local Optimum ◽  
Dynamic Adjustment ◽  
Exponential Variable

Ant colony algorithm (ACA) is employed and improved in routing protocol of information acquisition of manufacturing process based on wireless networks. Weighting factor for paths selection is defined as an exponential variable in adaptive ACA. In this way, it prevents the weighting factor from excess increasing or rapid reducing to 0 which results in local optimum. This approach can dynamically adjust paths selection and improve global search ability by optimizing global policy. Adaptive ACA consumes the least time in the process of searching the most optimized paths and searches the shortest paths under the same of iterative loops. Under the same condition of the information heuristic factor and the expected heuristic factor, the algorithm shows good adaptation, realizes the load balancing between paths and resolves the dynamic adjustment problem.

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.

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.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

scholarly journals Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

2020 ◽  
Vol 17 (3) ◽  
pp. 172988141989897 ◽  
Author(s):  
Shinan Zhu ◽  
Weiyi Zhu ◽  
Xueqin Zhang ◽  
Tao Cao
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Avoidance Strategy

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

An Improved Ant Colony Algorithm for the Shortest Path in City’s Road Network

2011 ◽  
Vol 121-126 ◽  
pp. 1296-1300 ◽  
Author(s):  
Jun Bi ◽  
Jie Zhang ◽  
Wen Le Xu
Keyword(s):  
Shortest Path ◽  
Road Network ◽  
Ant Colony Algorithm ◽  
Road Traffic ◽  
Global Optimum ◽  
Ant Colony ◽  
Selection Strategy ◽  
Ant Colony System ◽  
Local Optimum ◽  
Information Quantity

The shortest path between the start node and end node plays an important role in city’s road traffic network analysis system. The basic ant colony system algorithm which is a novel simulated evolutionary algorithm is studied to solve the shortest path problem. But the basic ant colony system algorithm is easy to run into the local optimum solution for shortest path. In order to solve the problem, the improved ant colony system algorithm is proposed. The improvement methods for selection strategy, local search, and information quantity modification of basic ant colony system are discussed in detail. The experiments are done in Beijing road network in China. The results of experiments show that comparing with the basic ant colony algorithm, the improved algorithm can easily converge at the global optimum for the shortest path.

scholarly journals Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Shanchen Pang ◽  
Kexiang Xu ◽  
Shudong Wang ◽  
Min Wang ◽  
Shuyu Wang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Virtual Machine ◽  
User Experience ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Local Optimum ◽  
Placement Method

Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.

scholarly journals Mobile robot path planning using an improved ant colony optimization

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877467 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Search Space ◽  
Step Length ◽  
Ant Colony ◽  
Dynamic Adjustment ◽  
Feedback Information ◽  
Intelligent Optimization Algorithm ◽  
Selection Of

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

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