Gene Therapy for Improving the Performance of Genetic Algorithm in Pipe Network Optimization

2009 ◽  
Author(s):  
A. Al-Khomairi ◽  
M.H. Imam
Keyword(s):  
Genetic Algorithm ◽  
Gene Therapy ◽  
Network Optimization ◽  
Pipe Network

scholarly journals Noncrossover Dither Creeping Mutation-Based Genetic Algorithm for Pipe Network Optimization

2014 ◽  
Vol 140 (4) ◽  
pp. 553-557 ◽  
Author(s):  
Feifei Zheng ◽  
Aaron C. Zecchin ◽  
Angus R. Simpson ◽  
Martin F. Lambert
Keyword(s):  
Genetic Algorithm ◽  
Network Optimization ◽  
Pipe Network

scholarly journals An Improved Genetic Algorithm for Pipe Network Optimization

1996 ◽  
Vol 32 (2) ◽  
pp. 449-458 ◽  
Author(s):  
Graeme C. Dandy ◽  
Angus R. Simpson ◽  
Laurence J. Murphy
Keyword(s):  
Genetic Algorithm ◽  
Network Optimization ◽  
Pipe Network ◽  
Improved Genetic Algorithm

Optimization of Pipe Network Design using Genetic Algorithm

2015 ◽  
Vol 5 (2) ◽  
pp. 31-38
Author(s):  
Prabhat Kumar Singh Dikshit ◽  
Keyword(s):  
Genetic Algorithm ◽  
Network Design ◽  
Pipe Network

scholarly journals Research on Intelligent Solution of Service Industry Supply Chain Network Optimization Based on Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yixin Zhou ◽  
Zhen Guo
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Network Optimization ◽  
Service Industry ◽  
User Behavior ◽  
Service Level ◽  
Supply Chain Network ◽  
Supply Network ◽  
Running Time ◽  
Average Running Time

With the advent of the era of big data (BD), people’’s living standards and lifestyle have been greatly changed, and people’s requirements for the service level of the service industry are becoming higher and higher. The personalized needs of customers and private customization have become the hot issues of current research. The service industry is the core enterprise of the service industry. Optimizing the service industry supply network and reasonably allocating the tasks are the focus of the research at home and abroad. Under the background of BD, this paper takes the optimization of service industry supply network as the research object and studies the task allocation optimization of service industry supply network based on the analysis of customers’ personalized demand and user behavior. This paper optimizes the supply chain network of service industry based on genetic algorithm (GA), designs genetic operator, effectively avoids the premature of the algorithm, and improves the operation efficiency of the algorithm. The experimental results show that when m = 8 and n = 40, the average running time of the improved GA is 54.1 s. The network optimization running time of the algorithm used in this paper is very fast, and the stability is also higher.

Sign in / Sign up

Export Citation Format

Share Document