Research on Adaptive Scheduling Algorithm Based on Improved Genetic Algorithm for Multifunctional Phased Array Radar

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.

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A course scheduling algorithm based on improved genetic algorithm with multi-objective constrains

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci.2019.8778493 ◽

2019 ◽

Author(s):

Cun-bo Jiang ◽

Hao Liu

Keyword(s):

Genetic Algorithm ◽

Scheduling Algorithm ◽

Improved Genetic Algorithm ◽

Multi Objective ◽

Course Scheduling

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1030-1032.1671 ◽

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.

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Blower Resource Scheduling Based on Elite Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.219-220.591 ◽

2011 ◽

Vol 219-220 ◽

pp. 591-595 ◽

Cited By ~ 1

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.

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