Research of Hybrid Mobile Agent Routing in Wireless Sensor Network

2013 ◽  
Vol 341-342 ◽  
pp. 1181-1186
Author(s):  
Li Hong Zhang ◽  
Shu Qian Chen
Keyword(s):  
Mobile Agent ◽  
Ant Colony Algorithm ◽  
Data Transfer ◽  
Average Energy ◽  
Optimal Solution ◽  
Global Search ◽  
Ant Colony ◽  
Feedback Information ◽  
Low Efficiency ◽  
Average Energy Consumption

The mobile agent route is essentially a multi-constraint optimization problem, Genetic Algorithms has fast random global search ability, but the feedback information of the system does not use and has the problem of low efficiency of finding exact solutions, propose a genetic hybrid ant colony algorithm for WSN mobile agent route. Use of the fast random global search capabilities of genetic algorithm to find better solutions, then the better solution replaced by the initial pheromone of the ant colony algorithm, finally use the advantages of convergence speed of ant colony algorithm to find the global optimal solution for mobile agent route. Simulation results show that the algorithm can find optimal mobile agent route in a relatively short time, relative to other routing algorithms, reducing network latency and average energy consumption, improving the speed and efficiency of data transfer.

Application Research of Improved Ant Colony Algorithm in Hybrid Mobile Agent Routing

2013 ◽  
Vol 734-737 ◽  
pp. 3093-3097
Author(s):  
Li Hong Zhang ◽  
Shu Qian Chen ◽  
Gui Zhi Bai
Keyword(s):  
Mobile Agent ◽  
Ant Colony Algorithm ◽  
Average Energy ◽  
Optimal Solution ◽  
Residual Energy ◽  
Ant Colony ◽  
Wireless Sensor Node ◽  
Feedback Information ◽  
Low Efficiency ◽  
Improved Ant Colony Algorithm

Mobile agent routing essentially is a multi-constraint optimization problem, for Ant Colony algorithm for global search capability is weak, the feedback information of the system does not use and has the problem of low efficiency of finding exact solutions, propose an improved Ant Colony Algorithm for WSN mobile agent routing. This algorithm takes full use of the wireless sensor node energy, the direction of ants is determined according to the residual energy and the distance of nodes, quickly find the global optimal solution for mobile agent route. Simulation results show that the algorithm can find optimal mobile agent route in a relatively short time, relative to other routing algorithms, reducing network latency and average energy consumption, effectively alleviate network congestion, and extend the network lifetime.

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.

Design of Vision Measurement Device for Seeding Robot based on Ant Colony Algorithm and Nonlinear Circuit System

2017 ◽  
Vol 12 (3) ◽  
pp. 42-50 ◽  
Author(s):  
Liu Xiaojie ◽  
Zhu Hongjin ◽  
Fan Honghui ◽  
Zhang Min
Keyword(s):  
Ant Colony Algorithm ◽  
Control Process ◽  
Border Crossing ◽  
Ant Colony ◽  
Nonlinear Circuit ◽  
Measurement Device ◽  
Vision Measurement ◽  
Low Efficiency

In order to solve the problem of low efficiency and accuracy in the control process of seeding robot, a vision measurement device based on ant colony algorithm and nonlinear circuit system is proposed in this paper. By scanning the trunks areas, border crossing points of the bottom of the tree and ground were detected, and these points were divided into two clusters on both sides based on neighbouring relationship. The simulation result is compared with artificial recognition in a two-orchard environment. The result shows that the proposed method is reliable, safe and can satisfy the moving request of seeding robot.

scholarly journals Multi-objective performance optimization of ORC cycle based on improved ant colony algorithm

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 48-59 ◽  
Author(s):  
Rong He ◽  
Xinli Wei ◽  
Nasruddin Hassan
Keyword(s):  
Performance Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Second Law Of Thermodynamics ◽  
Optimization Method ◽  
Ant Colony ◽  
Multi Objective ◽  
Objective Performance ◽  
Performance Evaluation Index ◽  
Improved Ant Colony Algorithm

Abstract To solve the problem of multi-objective performance optimization based on ant colony algorithm, a multi-objective performance optimization method of ORC cycle based on an improved ant colony algorithm is proposed. Through the analysis of the ORC cycle system, the thermodynamic model of the ORC system is constructed. Based on the first law of thermodynamics and the second law of thermodynamics, the ORC system evaluation model is established in a MATLAB environment. The sensitivity analysis of the system is carried out by using the system performance evaluation index, and the optimal working parameter combination is obtained. The ant colony algorithm is used to optimize the performance of the ORC system and obtain the optimal solution. Experimental results show that the proposed multi-objective performance optimization method based on the ant colony algorithm for the ORC cycle needs a shorter optimization time and has a higher optimization efficiency.

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.

Design of Vision Measurement Device for Seeding Robot based on Ant Colony Algorithm and Nonlinear Circuit System

2020 ◽  
pp. 400-408
Author(s):  
Liu Xiaojie ◽  
Zhu Hongjin ◽  
Fan Honghui ◽  
Zhang Min
Keyword(s):  
Ant Colony Algorithm ◽  
Control Process ◽  
Border Crossing ◽  
Ant Colony ◽  
Nonlinear Circuit ◽  
Measurement Device ◽  
Vision Measurement ◽  
Low Efficiency

In order to solve the problem of low efficiency and accuracy in the control process of seeding robot, a vision measurement device based on ant colony algorithm and nonlinear circuit system is proposed in this paper. By scanning the trunks areas, border crossing points of the bottom of the tree and ground were detected, and these points were divided into two clusters on both sides based on neighbouring relationship. The simulation result is compared with artificial recognition in a two-orchard environment. The result shows that the proposed method is reliable, safe and can satisfy the moving request of seeding robot.

Improved Ant Colony Algorithm for Optimal Solution TSP

2013 ◽  
Vol 765-767 ◽  
pp. 699-702
Author(s):  
Tian Yuan Zhou
Keyword(s):  
Traveling Salesman Problem ◽  
Ant Colony Algorithm ◽  
Search Strategy ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Algorithm Analysis ◽  
Simulation Testing ◽  
The Traveling Salesman Problem ◽  
Improved Ant Colony Algorithm

Based on the ant colony algorithm analysis and research, this paper proposed an improved ant colony algorithm. Through updating pheromone and optimal search strategy, then applied to the Traveling Salesman Problem (TSP), effectively improved the searching capability of the algorithm. Finally through the simulation testing and analysis, verified that the improved ant colony algorithm is effective, and has good performance.

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%.

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.

Wireless Sensor Network Mobile Agent Routing Based on the Improved Ant Colony Algorithm

2014 ◽  
Vol 587-589 ◽  
pp. 2339-2345
Author(s):  
Jia Yan Li ◽  
Jun Ping Wang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Mobile Agent ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Redundant Data

This paper proposes a new wireless sensor routing algorithm by combining the ant colony algorithm with the mobile agent technology. This algorithm considers the distance and path energy overhead among nodes and residual node energy, equalizes the energy overhead in the network, improves the update rule of the ant colony information elements and speeds up convergence of the ant colony algorithm to get the optimal values. The simulation results indicate that this algorithm can improve the globalization and convergence speed, effectively reduce redundant data transmission and communication overhead, extend the network lifecycle and be very suitable for a large-scale wireless sensor network compared to other mobile agent routing algorithms.

Sign in / Sign up

Export Citation Format

Share Document