Algorithm Research Based on Distributed Water Multi-Level Resources Scheduling

2014 ◽  
Vol 513-517 ◽  
pp. 2565-2568
Author(s):  
Yi Fan Yuan ◽  
Jiu Li Wang
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Resource Scheduling ◽  
Improved Genetic Algorithm ◽  
Scheduling Policy ◽  
Coding Scheme ◽  
Level Sequence ◽  
Multi Level ◽  
And Storage

With the rapid development of hydropower in China, there are a lot of reservoirs under constructions are put into operation. Therefore, resource scheduling of distributed water conservancy project has become a key focus in current researches. Based on distributed water multi-level resources, the paper put forward to apply the improved genetic algorithm to reservoir resource scheduling. In this way, water level sequence can be the basic genetic algorithm coding scheme, and storage status of reservoir can be stored with the array. Then the genetic algorithm coding can be operated based on the corresponding array index of each reservoir. The paper tries to prove the feasibility of this scheduling policy with some examples, simplifying the process of scheduling algorithm and providing guiding basis for water resource scheduling.

Blower Resource Scheduling Based on Elite Genetic Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 591-595 ◽  
Author(s):  
Guang Nian Yang ◽  
Wei Qi ◽  
Jun Zhou
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Scheduling Algorithm ◽  
Sewage Treatment ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Task Completion ◽  
Toxic Substances ◽  
Improved Genetic Algorithm ◽  
Grid Resource

Now, our sewage treatment industry mainly depends on the blower of aeration act as metabolic, absorbed in the toxic substances. Blower resources management is the key issue of sewage treatment. Traditional resource scheduling algorithm exist some defects, for example it can not well meet the quality requirements and can not get the optimal solution. This article gives a new resource scheduling method based on improved genetic algorithm. It achieves grid resource scheduling by using real number encoding and activities point crossover. Experiments show that genetic algorithm can reduce executing time and task completion time, and further improve the scalability of resource scheduling model. This algorithm has stability and high efficiency in grid environment.

Grid Resource Scheduling Strategy Based on Elite DNA Genetic Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 1594-1598
Author(s):  
Sheng Jun Xue ◽  
Wei Qi
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Grid Resource ◽  
Grid Environment ◽  
Grid Resource Scheduling

Traditional resource scheduling algorithm, in grid environment, exist some defects, for example it can not well meet the quality requirements and can not get the optimal solution. This article gives a new resource scheduling method based on improved genetic algorithm. It achieves grid resource scheduling by using real number encoding and activities point crossover. Experiments show that genetic algorithm can reduce executing time and task completion time, and further improve the scalability of resource scheduling model. This algorithm has stability and high efficiency in grid environment.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

scholarly journals Passive Location Resource Scheduling Based on an Improved Genetic Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2093 ◽  
Author(s):  
Jianjun Jiang ◽  
Jing Zhang ◽  
Lijia Zhang ◽  
Xiaomin Ran ◽  
Yanqun Tang
Keyword(s):  
Genetic Algorithm ◽  
Resource Scheduling ◽  
Passive Location ◽  
Improved Genetic Algorithm

scholarly journals Selection of Cold Chain Logistics Distribution Center Location Based on Improved Genetic Algorithm

2018 ◽  
Vol 227 ◽  
pp. 02018
Author(s):  
Jianchang Lu ◽  
Yaxin Zhao
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Rapid Development ◽  
Cold Chain ◽  
Distribution Model ◽  
Improved Genetic Algorithm ◽  
Actual Case ◽  
Logistics Industry ◽  
Penalty Cost ◽  
Logistics Distribution

With the rapid development of food refrigeration and freezing technology, food cryogenic storage and vehicle transportation scheduling technology, the cold chain logistics industry has entered a period of rapid development. According to the problem of urban cold chain distribution route, based on the vehicle distribution model with time window, the minimum cost of transportation, cost of energy, cost of goods, penalty cost is the objective function, and the urban cold chain logistics distribution path is established. Optimized mathematical model. According to the actual case, the analysis of the cold chain distribution model is carried out by using the analytical genetic algorithm, and the optimal combination of the distribution paths with the lowest total cost is obtained, which has certain reference significance for the urban cold chain logistics distribution route problem.

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