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

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.

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Study on Vehicle Routing and Scheduling Problems in Underground Mine Based on Adaptively ACA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.157-158.1293 ◽

2012 ◽

Vol 157-158 ◽

pp. 1293-1296 ◽

Cited By ~ 2

Author(s):

Qing Hua Gu ◽

Shi Gun Jing

Keyword(s):

Convergence Rate ◽

Vehicle Routing ◽

Ant Colony Algorithm ◽

Optimal Solution ◽

Underground Mine ◽

Ant Colony ◽

Test Case ◽

Scheduling Problems ◽

Routing And Scheduling ◽

Improved Ant Colony Algorithm

For vehicle routing optimization problem in the underground mine, a famous NP- Hard problem is put forward. This paper uses improved ant colony algorithm (ACA) to solve the problem. Basic ant colony algorithm (ACA) has many shortages, such as long searching time， slow convergence rate and easily limited to local optimal solution etc. The improved ant colony algorithm is proposed to overcome these shortcomings and improve its performance adaptively. In every iteration of the ant colony algorithm, adaptive evaporating coefficient is selected to control the convergence rate at first. And the power of this approach was demonstrated on a test case. The results derived from basic ACA and the improved ACA are compared and analyzed in the experiment. It proved that the improved ant colony algorithm is effective

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

Computational Intelligence and Neuroscience ◽

10.1155/2021/3115704 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.587-589.2339 ◽

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.

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A Improved Pareto of Ant Colony Algorithm to Solve the Vehicle Routing Problem with Time Windows

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1030-1032.1941 ◽

2014 ◽

Vol 1030-1032 ◽

pp. 1941-1944

Author(s):

Hong Dou Zhang ◽

Ning Guo ◽

Jian Lin Mao ◽

Hai Feng Wang

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Ant Colony Algorithm ◽

Transition Probability ◽

Time Windows ◽

Optimal Solution ◽

Ant Colony ◽

Local Optimum ◽

Routing Problem ◽

Important Extension

Vehicle routing problem with time Windows (VRPTW) that is a kind of important extension type for VPR. In view of problem which the ant colony algorithm in solving VRPTW easily plunged into local optimum , this paper defines a new ant transition probability of saving ideas, and uses the Pareto optimal solution set of global pheromone updating rule, and puts forward a kind of improved Pareto ant colony algorithm (IPACA) . Through the simulation experiments show that IPACA improves the global search ability of ACA, effectively avoids the algorithm falls into local optimum, and reduces the total distribution cost (distance), so as to verify the effectiveness of the proposed algorithm.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.680.39 ◽

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.

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Design of Trusted Security Routing in Wireless Sensor Networks Based on Quantum Ant Colony Algorithm

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v13i07.7273 ◽

2017 ◽

Vol 13 (07) ◽

pp. 4

Author(s):

Xiaobin Shu ◽

Caihong Liu ◽

Chunxia Jiao ◽

Qin Wang ◽

Hongfeng Yin

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Wireless Sensor Network ◽

Sensor Networks ◽

Large Scale ◽

Ant Colony Algorithm ◽

Routing Algorithm ◽

Ant Colony ◽

Wireless Sensor ◽

Font Size

To design an effective secure routing of trusted nodes in wireless sensor networks, quantum ant colony algorithm is applied to the design of large-scale wireless sensor network routing. The trustworthy network is used as the pheromone distribution strategy.Then, the pheromone is encoded by the quantum bit. The pheromone is updated by the quantum revolving door, and the energy consumption prediction is carried out to select the path. Finally, the trusted security routing algorithm of the wireless sensor network based on the global energy balance is realized. The quantum ant colony algorithm is superior to the traditional ant colony algorithm in algorithm convergence speed and global optimization. It can balance the energy consumption of the network node and can effectively resist the attacks such as Wormholes.It is very promising to apply the quantum ant colony algorithm to the routing algorithm of large scale wireless sensor networks.

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

Mathematical Problems in Engineering ◽

10.1155/2013/785383 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 1

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.

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Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

Sustainability ◽

10.3390/su11113197 ◽

2019 ◽

Vol 11 (11) ◽

pp. 3197 ◽

Cited By ~ 6

Author(s):

Shiqi Tian ◽

Shijie Wang ◽

Xiaoyong Bai ◽

Dequan Zhou ◽

Guangjie Luo ◽

...

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Metal Content ◽

Ant Colony Algorithm ◽

Hyperspectral Data ◽

Ant Colony ◽

Guizhou Province ◽

Neural Network Models ◽

Prediction Ability ◽

Bp Model

The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12–64%. The prediction ability of the model of the four metals is Cr > Co > Sb > Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.

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