scholarly journals Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Junjie Liu
Keyword(s):  
Neural Network ◽  
Local Minimum ◽  
Ant Colony Algorithm ◽  
Similarity Judgment ◽  
Ant Colony ◽  
Slow Convergence ◽  
Quantum Genetic Algorithm ◽  
Constant Change ◽  
Acquisition Method ◽  
Selection Of

Nowadays, with the constant change of public aesthetic standards, a large number of new types and themes of film programs have emerged. For this reason, this paper proposes an improved neural network optimized by mutation ant colony algorithm for automatic acquisition of film labels, which not only overcomes the disadvantages of traditional neural network, such as difficulty in determining weights, slow convergence speed, and easiness to fall into local minimum, but also makes up for the shortcomings faced by using ant colony algorithm alone through the gradient information of quantum genetic algorithm neural network. The results show that the user similarity judgment is added in the process of calculating the user rating deviation between movies, and the neighbor chooses to add the movie tag weight and rating similarity as the basis for the neighbor selection of the target movie in the process of predicting the target movie rating. Experiments show the effectiveness of the algorithm.

scholarly journals Study of Wireless Sensor Network Route Based on Improved Ant Colony Algorithm

2016 ◽  
Vol 12 (10) ◽  
pp. 86 ◽  
Author(s):  
Jingyi Bo ◽  
Yubin Wang ◽  
Na Xu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Ant Colony Algorithm ◽  
Low Cost ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Font Size ◽  
Energy Equilibrium ◽  
Route Algorithm ◽  
Selection Of

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: SimSun; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Combining the characteristics of wireless sensor network, the ant colony algorithm is applied to a wireless sensor network, and a wireless sensor network route algorithm based on energy equilibrium is proposed in this paper. This algorithm takes the energy factor into the consideration of selection of route based on probability and enhanced calculation of information so as to find out the optimal route from the source node to the target node with low cost and balanced energy, and it prolongs the life cycle of the whole network</span><span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: SimSun; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">.</span>

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.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

Application of Ant Colony Algorithms and Neural Networks in Fault Diagnosis

2014 ◽  
Vol 556-562 ◽  
pp. 3014-3017
Author(s):  
Jing Bo Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony Algorithms ◽  
Weak Points ◽  
Diagnosis Method ◽  
Computational Simplicity ◽  
Minimum Points ◽  
Neural Network Technology

Neural network technology is widely applied due to its computational simplicity and versatility. But, this method has some weak points, for example, slow convergence, less accurate and easy to fall into local minimum points. Combined ant colony algorithm and neural network for fault diagnosis, it can overcome the limitations of a single fault diagnosis method. Ant colony neural network method is applied to gearbox fault diagnosis, the results show that the diagnosis with characteristics of high precision, strong scientific and practical wider.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

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