scholarly journals Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weichuan Ni ◽  
Zhiming Xu ◽  
Jiajun Zou ◽  
Zhiping Wan ◽  
Xiaolei Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Performance ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Routing Optimization ◽  
Ipv6 Network

The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user’s needs for network service quality, network performance, and other aspects.

scholarly journals Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shaopei Chen ◽  
Ji Yang ◽  
Yong Li ◽  
Jingfeng Yang
Keyword(s):  
Neural Network ◽  
Vehicle Routing ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Neural Network Models ◽  
Network Systems

Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems.

scholarly journals Intelligent Bionic Optimization Algorithm Based on the Growth Characteristics of Tree Branches

2021 ◽  
Vol 15 (2) ◽  
pp. 34-46
Author(s):  
Fei Tang
Keyword(s):  
Genetic Algorithm ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Morphological Characteristics ◽  
Optimal Solution ◽  
Shoot Tip ◽  
Experimental Results ◽  
Ant Colony ◽  
The Individual

To improve the performance of bionic algorithms, an intelligent bionic optimization algorithm is proposed based on the morphological characteristics of trees growing toward light. The growth organ of the tree is mapped into the coding of the tree growth algorithm, and the entire tree is formed by selecting the fastest growing individual to form the next level of the tree. When the tree growth reaches a certain level, the individual code of the shoot tip is added to enhance the search ability of the individual shoot tip in the growth space of the entire tree. This method achieves a near-optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function. The experimental results show that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm or the ant colony algorithm.

scholarly journals Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 311 ◽  
Author(s):  
Hai Xue ◽  
Kyung Kim ◽  
Hee Youn
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Network Performance ◽  
Optimal Path ◽  
Optimal Solution ◽  
Software Defined Networking ◽  
Ant Colony ◽  
Centralized Control ◽  
Trip Time ◽  
Overall Performance

Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.

The Prediction Model of the Mechanical Properties of Composite Materials Based on Ant Colony Neural Network

2013 ◽  
Vol 680 ◽  
pp. 39-43
Author(s):  
Jing Wang ◽  
Jie Zhu ◽  
Qian Zhang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Composite Materials ◽  
Prediction Model ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Practical Applications ◽  
Global Optimal ◽  
Adaptive Ability

In this paper, a prediction model of the mechanical properties of composite materials has been proposed based on the ant colony neural network. The mechanical properties of the materials are the common problems that the various materials must be involved in the practical applications. The testing of the mechanical properties of the composite materials is of great significance to the development and the progress of the theory and the practice of composite materials. The ant colony algorithm takes advantage of the optimization mechanisms of ant colony, which has a strong ability to find the global optimal solution. The candidate group mechanism is added in the ant colony algorithm and the weights of the artificial neural network are trained through using the improved ant colony algorithm. This model has a strong adaptive ability and can be used in the prediction of the mechanical properties of composite materials. Then, the efficiency of the testing of mechanical properties can be improved.

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 Algorithm in QoS Routing Optimization

2010 ◽  
Vol 108-111 ◽  
pp. 353-358 ◽  
Author(s):  
Xiu Ju Liu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Qos Routing ◽  
Ant Colony ◽  
Design And Implementation ◽  
Routing Optimization ◽  
Speed Up ◽  
Simulation Results ◽  
Full Proof ◽  
Implementation Steps

QoS routing for the characteristics of the ant colony algorithm is improved. First of all defect from the ant colony algorithm and to increase Network Node bound, As well as to speed up global convergence analysis of the three areas of the ant colony algorithm to improve thinking; And by improving the ant colony algorithm in the QoS routing optimization application, the details of the ant colony algorithm to improve the design and implementation steps. Finally, simulation results show that: According to the algorithm for solving the problem and get the optimal solution of the ratio of full proof of improvement in the ant colony algorithm QoS routing optimization on the effectiveness and stability.

An Anycast Routing Algorithm Based on the Combination of Genetic Algorithm and Ant Colony Algorithm

2012 ◽  
Vol 239-240 ◽  
pp. 1324-1330 ◽  
Author(s):  
Tao Shen Li ◽  
Zhang Cai Li
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Ant Colony Algorithm ◽  
Initial Period ◽  
Optimal Path ◽  
Ant Colony ◽  
Routing Problem ◽  
Anycast Routing ◽  
Multiple Qos Constraints ◽  
Multiple Qos

To solve anycast routing problem with multiple QoS constraints, a improved hybrid algorithm which combines genetic algorithm and ant colony algorithm is proposed. In the initial period of hybrid algorithm, genetic algorithm was used to distribute pheromones in links and code and optimize control parameters of ant colony algorithm. Through judgment function, this algorithm can judge the time to combine the genetic algorithm with ant colony algorithm, and initialize the pheromones and start the ant colony algorithm at the last period of hybrid algorithm. To avoid hybrid algorithm falling into local optimal solution, a mutation operator was introduced in algorithm hybrid to update local pheromones of new path produced by mutation operation and reduced pheromones concentration on optimal path in time. The NS2 simulation results show that this algorithm can commendably solve the anycast routing problem with multiple QoS constraints, and its performance is better than other two algorithms.

scholarly journals An Improved Ant Colony Algorithm and Its Application in Vehicle Routing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Min Huang ◽  
Ping Ding
Keyword(s):  
Path Planning ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Optimal Path ◽  
Optimal Solution ◽  
Planning Method ◽  
Ant Colony ◽  
Routing Problem

Optimal path planning is an important issue in vehicle routing problem. This paper proposes a new vehicle routing path planning method which adds path weight matrix and save matrix. The method uses a new transition probability function adding the angle factor function and visibility function, while setting penalty function in a new pheromone updating model to improve the accuracy of the route searching. Finally, after each cycle, we use 3-opt method to update the optimal solution to optimize the path length. The results of comparison also confirm that this method is better than the traditional ant colony algorithm for vehicle routing path planning method. The result of computer simulation confirms that the method can plan a more rational rescue path focused on the real traffic situation.

The improved intelligent bionic optimization algorithm based on the growth characteristics of tree branches

2021 ◽  
pp. 1-9
Author(s):  
Fei Tang
Keyword(s):  
Genetic Algorithm ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Growth Characteristics ◽  
Ant Colony ◽  
Optimization Function ◽  
Growth Space ◽  
The Individual

To improve the optimization efficiency of the intelligent bionic optimization algorithm, this paper proposes intelligent bionic optimization algorithm based on the growth characteristics of tree branches. Firstly, the growth organ of the tree is mapped into the coding of the tree growth algorithm (intelligent bionic optimization algorithm). Secondly, the entire tree, that is the growing tree, is formed by selecting the individual that grows fast to generate the next level of shoot population. Lastly, if the growing tree reaches a certain level, the individual coding of the shoots is added to enhance the searching ability of the individuals of current generation in the growth tree growth space, so that the algorithm approaches the optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function and showed that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm and the ant colony algorithm.

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.

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