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.

Improved Ant Colony Algorithm Based on PSO and its Application on Cloud Computing Resource Scheduling

2014 ◽  
Vol 989-994 ◽  
pp. 2192-2195 ◽  
Author(s):  
Hai Yang
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Simulation Experiment ◽  
Resource Scheduling ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm

This paper introduces PSO algorithm into ant colony optimization algorithm so that an improved ant colony optimization algorithm named ACA-PSO is proposed. The ACA-PSO algorithm can get more effective optimal solutions by using PSO algorithm to do crossover operation and mutation operation so as to avoid trapping in local optimum. Finally, the simulation experiment reflects that the ACA-PSO algorithm speeds the convergence up which is more suitable for resource scheduling in cloud computing.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

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 Application on the Problem of the Improved Ant Colony Algorithm on Cloud Computing Scheduling

2018 ◽  
Vol 11 (5) ◽  
pp. 79-90
Author(s):  
Zhi-hui Shang ◽  
Jian-wei Zhang ◽  
Xiao-hua Wang ◽  
Hong-jin Li ◽  
Xu Luo
Keyword(s):  
Cloud Computing ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Improved Ant Colony Algorithm

Weaving scheduling based on an improved ant colony algorithm

2020 ◽  
pp. 004051752094889
Author(s):  
Wentao He ◽  
Shuo Meng ◽  
Jing’an Wang ◽  
Lei Wang ◽  
Ruru Pan ◽  
...  
Keyword(s):  
Production Scheduling ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Superior Performance ◽  
Ant Colony System ◽  
Local Optimum ◽  
Scheduling Method ◽  
Automatic Methods ◽  
Automatic Scheduling ◽  
Improved Ant Colony Algorithm

Weaving enterprises are faced with problems of small batches and many varieties, which leads to difficulties in manual scheduling during the production process, resulting in more delays in delivery. Therefore, an automatic scheduling method for the weaving process is proposed in this paper. Firstly, a weaving production scheduling model is established based on the conditions and requirements during actual production. By introducing flexible model constraints, the applicability of the model has been greatly expanded. Then, an improved ant colony algorithm is proposed to solve the model. To address the problem of the traditional ant colony algorithm that the optimizing process usually traps into local optimum, the proposed algorithm adopts an iterative threshold and the maximum and minimum ant colony system. In addition, the initial path pheromone distribution is formed according to the urgency of the order to balance each objective. Finally, the simulation experiments confirm that the proposed method achieves superior performance compared with manual scheduling and other automatic methods. The proposed method shows a certain guiding significance for weaving scheduling in practice.

Adaptive Cloud Resource Scheduling Model Based on Improved Ant Colony Algorithm

2020 ◽  
Vol 57 (1) ◽  
pp. 010603
Author(s):  
聂清彬 Nie Qingbin ◽  
潘峰 Pan Feng ◽  
吴嘉诚 Wu Jiacheng ◽  
曹耀钦 Cao Yaoqin
Keyword(s):  
Ant Colony Algorithm ◽  
Resource Scheduling ◽  
Ant Colony ◽  
Model Based ◽  
Scheduling Model ◽  
Cloud Resource ◽  
Cloud Resource Scheduling ◽  
Improved Ant Colony Algorithm
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