An improved genetic algorithm for degree constrained minimum spanning trees

2016 ◽  
Author(s):  
Kai Shi ◽  
Qingfeng Song ◽  
Sheng Lin ◽  
Guangping Xu ◽  
Zhanxu Cao
Keyword(s):  
Genetic Algorithm ◽  
Spanning Trees ◽  
Minimum Spanning Trees ◽  
Improved Genetic Algorithm

scholarly journals An Improved Genetic Algorithm for the Large-Scale Rural Highway Network Layout

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Changxi Ma ◽  
Cunrui Ma ◽  
Qing Ye ◽  
Ruichun He ◽  
Jieyan Song
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Spanning Trees ◽  
Polynomial Algorithms ◽  
Improved Genetic Algorithm ◽  
Exchange Method ◽  
Geographically Dispersed ◽  
Network Layout ◽  
Highway Network ◽  
Rural Highway

For the layout problem of rural highway network, which is often characterized by a cluster of geographically dispersed nodes, neither the Prim algorithm nor the Kruskal algorithm can be readily applied, because the calculating speed and accuracy are by no means satisfactory. Rather than these two polynomial algorithms and the traditional genetic algorithm, this paper proposes an improved genetic algorithm. It encodes the minimum spanning trees of large-scale rural highway network layout with Prufer array, a method which can reduce the length of chromosome; it decodes Prufer array by using an efficient algorithm with time complexityo(n)and adopting the single transposition method and orthoposition exchange method, substitutes for traditional crossover and mutation operations, which can effectively overcome the prematurity of genetic algorithm. Computer simulation tests and case study confirm that the improved genetic algorithm is better than the traditional one.

scholarly journals Improved Genetic Algorithm for Distribution System Performance Analysis by Taking Advantage of Essential Spanning Trees

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Yanzhu Ji ◽  
Zhuoqun Shi ◽  
Robert M. O’Connell
Keyword(s):  
Genetic Algorithm ◽  
Distribution System ◽  
Spanning Trees ◽  
Distribution Automation ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Small Test ◽  
Distribution System Reconfiguration ◽  
System Configurations ◽  
Automation Tools

Growing interest in the smart grid, increasing use of distributed generation, and classical distribution system reconfiguration (DSR) and restoration problems have led to the search for efficient distribution automation tools. One such tool, the improved Fast Nondominated Sorting Genetic Algorithm (FNSGA), not only is effective in finding system configurations that are optimal with respect to voltages, currents, and losses, but also considered parametric study to determine minimum values of N and Gen. In this paper, the essential spanning tree concept is expanded to improve the computational efficiency of the algorithm. Results of the study show that for relatively small test systems, optimum system configurations are obtained using values of N and Gen that require very small CPU times. In larger systems, optimum values of N and Gen requiring reasonable CPU times can also be found, provided that certain carefully chosen branches are removed from the pool of possibilities when producing the initial population in the algorithm. By using essential trees, the efficiency of the calculation is improved.

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.

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