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An Improved Genetic Algorithm for Pipe Network Optimization
Author(s) -
Dandy Graeme C.,
Simpson Angus R.,
Murphy Laurence J.
Publication year - 1996
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/95wr02917
Subject(s) - mathematical optimization , bitwise operation , algorithm , genetic algorithm , fitness function , adjacency list , operator (biology) , mutation , computer science , set (abstract data type) , penalty method , nonlinear programming , mathematics , nonlinear system , biochemistry , chemistry , physics , quantum mechanics , transcription factor , gene , repressor , programming language
An improved genetic algorithm (GA) formulation for pipe network optimization has been developed. The new GA uses variable power scaling of the fitness function. The exponent introduced into the fitness function is increased in magnitude as the GA computer run proceeds. In addition to the more commonly used bitwise mutation operator, an adjacency or creeping mutation operator is introduced. Finally, Gray codes rather than binary codes are used to represent the set of decision variables which make up the pipe network design. Results are presented comparing the performance of the traditional or simple GA formulation and the improved GA formulation for the New York City tunnels problem. The case study results indicate the improved GA performs significantly better than the simple GA. In addition, the improved GA performs better than previously used traditional optimization methods such as linear, dynamic, and nonlinear programming methods and an enumerative search method. The improved GA found a solution for the New York tunnels problem which is the lowest‐cost feasible discrete size solution yet presented in the literature.