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

Improved Genetic Algorithm for Optimization of Multi-Source Pumping Tree-Type Pipe Network

2011 ◽  
Vol 402 ◽  
pp. 738-741
Author(s):  
Hong Zhan ◽  
Jian Jun Yang ◽  
Yu Cheng Ding
Keyword(s):  
Genetic Algorithm ◽  
Graph Theory ◽  
Operating Cost ◽  
Dual Coding ◽  
Pipe Diameter ◽  
Pipe Network ◽  
Improved Genetic Algorithm ◽  
Binary Coding ◽  
Complete Optimization ◽  
Tree Type

An optimization model for multi-source pumping tree-type pipe network is established, in which the investment and operating cost are considered simultaneously. Useing graph theory knowledge, the essence of pipe layout optimization for multi-sources tree-type pipe network is searching for a group of optimal subtrees. In the complete optimization of pipe network, the pipe layout mode and pipe diameter are needed to optimize simultaneously, so the dual coding genetic algorithm combining binary coding with integer coding is adopted, and the pipe diameter restriction is satisfied automatically. According to the tree character, method based on cycle is adopted in the operational process of binary coding, the partheno-genetic operation is designed to produce new individual, the infeasible solutions are avoided. Example shows the efficiency of the algorithm.

BP Network Optimization Based on Improved Genetic Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1757-1763
Author(s):  
Wei Wei Sun ◽  
Yun Fei Yao ◽  
Chun Sheng Wang ◽  
Ye Gang Hu
Keyword(s):  
Genetic Algorithm ◽  
Network Optimization ◽  
Network Architecture ◽  
Training Method ◽  
Improved Genetic Algorithm ◽  
Bp Network ◽  
Network Training ◽  
Hierarchical Code ◽  
Adaptive Crossover ◽  
Crossover And Mutation

In view of the virtue and shortage of genetic algorithm and BP network, this paper proposes a new BP network training method based on improved genetic algorithm (IGA-BP). This algorithm uses hierarchical code, adaptive crossover and mutation, pruning similar chromosomes, dynamic supply new chromosomes and other operations, so the network structure and weight are optimized at the same time and the "premature" phenomenon is avoided. The simulation results show that the IGA-BP network architecture is simple, the convergence rate is quick, and has good approximation and generalization ability.

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
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