Application of an Improved Genetic Algorithm in Tourism Route Planning

2020 ◽  
pp. 137-145
Author(s):  
Xiaoyan Chen ◽  
Kun Zhang ◽  
Haifeng Wang ◽  
Jing Chen ◽  
Bhatti Uzair Aslam
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Improved Genetic Algorithm

scholarly journals Multi-UAV cooperative Route planning based on decision variables and improved genetic algorithm

2021 ◽  
Vol 1941 (1) ◽  
pp. 012012
Author(s):  
Jie Zhang ◽  
Ningzhou Li ◽  
Danyu Zhang ◽  
Xiaojuan Wei ◽  
Xiaojuan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Improved Genetic Algorithm ◽  
Decision Variables ◽  
Multi Uav

scholarly journals An Improved Genetic Algorithm for Island Route Planning

2017 ◽  
Vol 174 ◽  
pp. 433-441 ◽  
Author(s):  
Miao Gao ◽  
Guoyou Shi ◽  
Weifeng Li ◽  
Yuchuang Wang ◽  
Dongdong Liu
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Improved Genetic Algorithm

scholarly journals Research on E-commerce Logistics Distribution Path Planning Based on Improved Genetic Algorithm

2022 ◽  
Vol 16 ◽  
pp. 202-208
Author(s):  
Lan Lan
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Rapid Development ◽  
Optimal Path ◽  
Route Planning ◽  
Planning Method ◽  
Improved Genetic Algorithm ◽  
Logistics Distribution ◽  
Centralized Planning ◽  
Distribution Efficiency

With the rapid development of the Internet, e-commerce business has gradually emerged. However, its logistics distribution route planning method has problems such as redundancy of logistics data, which cannot achieve centralized planning of distribution paths, resulting in low e-commerce logistics distribution efficiency and long distribution distances, higher cost. Therefore, in order to improve the ability of logistics distribution path planning, this paper designs an e-commerce logistics distribution path planning method based on improved genetic algorithm. Optimize the analysis of e-commerce logistics distribution nodes, establish a modern logistics distribution system, and optimize the total transportation time and transportation cost under the location model of the logistics distribution center. Using hybrid search algorithm and improved genetic algorithm parameters, an improved genetic algorithm distribution path planning model is established to select the optimal path of logistics distribution, and realize e-commerce logistics distribution path with high accuracy, low error and good convergence. planning. According to the experimental results, the method in this paper can effectively shorten the distance of e-commerce logistics distribution path, reduce the number of distribution vehicles, reduce distribution costs, improve distribution efficiency, and effectively achieve centralized planning of logistics distribution. Therefore, the e-commerce logistics distribution route planning method based on improved genetic algorithm has high practical application value.

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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