Advanced ant colony algorithm for high dimensional abnormal data mining in Internet of things

2022 ◽  
pp. 1-10
Author(s):  
Huixian Wang ◽  
Hongjiang Zheng
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Ant Colony ◽  
High Dimensional ◽  
Average Error ◽  
Mining Depth ◽  
The Internet Of Things ◽  
Improved Ant Colony Algorithm

This paper proposes a deep mining method of high-dimensional abnormal data in Internet of things based on improved ant colony algorithm. Preprocess the high-dimensional abnormal data of the Internet of things and extract the data correlation feature quantity; The ant colony algorithm is improved by updating the pheromone and state transition probability; With the help of the improved ant colony algorithm, the feature response signal of high-dimensional abnormal data in Internet of things is extracted, the judgment threshold of high-dimensional abnormal data in Internet of things is determined, and the objective function is constructed to optimize the mining depth, so as to realize the deep data mining. The results show that the average error of the proposed method is only 0.48%.

Improved Ant Colony Algorithm Using in Design of the New Internet of Things Storage Mailbox System

2013 ◽  
Vol 753-755 ◽  
pp. 2845-2848
Author(s):  
Ke Wang Huang
Keyword(s):  
Internet Of Things ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
The Internet ◽  
User Selection ◽  
The Internet Of Things ◽  
Improved Ant Colony Algorithm ◽  
Selection Of

The paper focuses on improved ant colony algorithm using in design of the internet of things storage and mailbox system. The improved ant colony algorithm solves the problems of the location of the storage mailbox mounting and user selection of optimal delivery.

scholarly journals Cross-Modal Travel Route Recommendation Algorithm Based on Internet of Things Awareness

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuran Zhang ◽  
Ziyan Tang
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Ant Colony Algorithm ◽  
Daily Life ◽  
Ant Colony ◽  
The Internet ◽  
Evaluation Score ◽  
Route Recommendation ◽  
The Internet Of Things ◽  
Greedy Solution

In recent years, the Internet of Things has developed rapidly in people’s lives. This brand-new technology is flooding people’s lives and widely used in many fields, such as medical field, science and technology field, and industry and agriculture field. As a modern technology, the Internet of Things has many characteristics of low power consumption and multifunction, and it also has the characteristics of data-aware computing. This is the characteristic of this new product. In people’s daily life, the Internet of Things is also closely related to people’s daily life. In the tourism industry, the Internet of Things can make the best use of everything and give full play to its various advantages as much as possible. The Internet of Things can perceive cross-modal tourism routes. So here, this paper summarizes various algorithms recommended by the Internet of Things for this tourist route and works out the experimental data methods of these algorithms for cross-modal tourism route recommendation. The proposed algorithm is verified by data simulation, compared with related algorithms. We analyze and summarize the simulation results. At present, there is no comparative analysis of the performance of ant colony algorithm, genetic algorithm, and its optimization algorithm in tourism route recommendation. On the basis of crawling the tourism data in the Internet, this paper applies ant colony algorithm, genetic algorithm, max–min optimization ant colony algorithm, and hybrid ant colony algorithm based on greedy solution to tourism route recommendation and evaluates and compares the algorithms from three aspects: average evaluation score, optimal evaluation score, and algorithm time. Experimental results show that the max–min optimization ant colony algorithm and the hybrid ant colony algorithm based on greedy solution can be effectively applied to automated tourist route recommendation.

scholarly journals Design of Intelligent Building Scheduling System for Internet of Things and Cloud Computing

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Tiangang Wang ◽  
Zhe Mi
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real Time ◽  
High Speed ◽  
Ant Colony Algorithm ◽  
Resource Scheduling ◽  
Ant Colony ◽  
Local Optimum ◽  
The Real ◽  
Improved Ant Colony Algorithm

The cloud computing (CC) and Internet of Things (IoT) are widely utilized and provided for intelligent perception and on-demand utilization like industries and public areas. The full sharing, free circulation and various manufacturing resources allocation are investigated in manufacturing. In order to ensure the real-time and effectiveness of resource storage scheduling in Internet of things information system, there are many kinds and quantities of building equipment. An improved ant colony algorithm is presented to remove the shortcomings of the existing ant colony algorithm with slow speed and fall into local optimum. The improved ant colony algorithm is transplanted into cloud computing environment. The advantages of fast computing and high speed storage of cloud computing can realize the real-time resource scheduling of building equipment. The experimental results present that the improved ant colony algorithm can obviously improve the efficiency of resource scheduling in cloud computing environment.All the experiments are performed on the MATLAB.

scholarly journals Application of an Improved Ant Colony Algorithm to Optimal Path Planning for Automatic Guided Vehicles

2020 ◽  
Author(s):  
weikang zhu ◽  
jicheng liu
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Optimal Solution ◽  
Random Selection ◽  
Ant Colony ◽  
Smoothing Factor ◽  
Local Optimal Solution ◽  
Selection Operator ◽  
Improved Ant Colony Algorithm

Abstract The path planning is the key technology of AGV path finding. This paper uses an improved ant colony algorithm to plan the path of an AGV. For avoiding the defects of traditional ant colony algorithm such as low smoothness of route and local optimal solution, the transition probability and pheromone update method are improved. Various actual turning situations are analyzed in the transition probability, the basis for defining the smoothing factor is provided by the Bezier curve, and a random selection operator is formed for updating local pheromone by extracting characteristic information of iterative process. The simulation results in different environments prove that the smoothing factor plays an important role in optimizing the smoothness of the path and the diversity of the constructed solutions, and the random selection operator is effective in solving the contradiction of the local optimal solution and in finding the optimal solution.

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