scholarly journals An Efficient Task Scheduling Method for the Unified Interface Platform of the Electric Information Acquisition System

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ye Fang Bin
Keyword(s):  
Task Scheduling ◽  
Information Acquisition ◽  
Optimal Solution ◽  
Minimum Variance ◽  
Acquisition System ◽  
Combination Method ◽  
Business Systems ◽  
Target Task ◽  
Static Data ◽  
Local Optimal Solution

Due to the large and frequent static data interaction between the Electric Information Acquisition System and the external business systems, researching on using limited server sources to do an efficient task scheduling is becoming one of the key technologies of the unified interface platform. The information interaction structure of the unified interface platform is introduced. Task scheduling has been decomposed into two stages, task decomposition and task combination, based on the features (various types and dispersed) of large static data. The principle of the minimum variance of the subtasks data quantity is used to do the target task resolving in the decomposition stage. The thought of the Greedy Algorithm is used in the taskcombination. Breaking the target task with large static data into serval composed tasks with roughly same data quantity is effectively realized. Meanwhile, to avoid the situation of the GA falling into the local optimal solution, an improved combination method has been put forward. Moreover, the new method creates more average composed tasks and making the task scheduling more effective. Ultimately, the effectiveness of the proposed method is verified by the experimental data.

The Scheduling Algorithm of Grid Task Based on Cloud Model

2010 ◽  
Vol 439-440 ◽  
pp. 1177-1183
Author(s):  
Shu Tao Gao
Keyword(s):  
Task Scheduling ◽  
Cloud Model ◽  
Excess Entropy ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Simulation Experiments ◽  
Cloud Droplets ◽  
Local Optimal Solution ◽  
Characteristic Values ◽  
Scheduling Optimization Algorithm

In this paper, a kind of grid task scheduling optimization algorithm based on cloud model is proposed with the characteristics of cloud model. With the target being the cloud droplets of the cloud model, this algorithm gets three characteristic values of cloud through the reverse cloud: expectations, entropy and excess entropy, and then obtains cloud droplets using the forward cloud algorithm by adjusting the values of entropy and excess entropy. After several iterations, it achieves the optimal solution of task scheduling. Theoretical analysis and results of simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources and avoids such problems as the local optimal solution of genetic algorithms and premature convergence caused by too much selection pressure with higher accuracy and faster convergence.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

The Design and Implementation of an Information Acquisition System for Building Running Environment Based on Wireless Sensor Network and Loongson2F

2013 ◽  
Vol 303-306 ◽  
pp. 578-581
Author(s):  
Kai Tuo Du ◽  
Zhen Ya Zhang ◽  
Hong Mei Cheng ◽  
Qian Sheng Fang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Information Acquisition ◽  
Host Computer ◽  
Acquisition System ◽  
Wireless Sensor ◽  
Hardware Cost ◽  
Public Building ◽  
Design And Implementation ◽  
Wireless Node

The process of building environmental information perception can be constructed with wireless sensor network (WSN) expediently. A WSN based information acquisition system for building running environment is described in this paper. The CPU of host computer in the system is Loongson2F and wireless nodes in the system are implemented as Telsob nodes. Because the price of wireless node is low, the hardware cost of the desired system is decreased evidently and the security of the desired system is enhanced because the CPU of the host is native. With those features, the application scenario of the desired system is extended widely. To verify the suitability of the using of Collection Tree Protocol (CTP) in construction of the WSN in the desired information acquisition system, the performance of the CTP based WSN deployed in public building space for environment information acquiring are tested and solutions for some key problems in the construction and the maintenance of the CTP based WSN are given in this paper too.

Research and Improvement on K-Means Clustering Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3231-3235
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Clustering Center ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

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