Showing 2 results for Pipe Networks
M.h. Afshar, M.r. Ghasemi,
Volume 3, Issue 2 (6-2005)
Abstract
An efficient selection operator for use in genetic search of pipe networks optimal design
is introduced in this paper. The proposed selection scheme is the superior member of a family of
improved selection operators developed in an attempt to more closely simulate the main features of
the natural mating process which is not reflected in existing selection schemes. The mating process
occurring in the nature exhibits two distinct features. First, there is a competition between
phenotypes looking for the fittest possible mate which usually ends up with choosing a mate with
more or less the same fitness. Second, and more importantly, the search for a mate is often confined
to a community of phenotypes rather than the whole population. Four different selection operators
simulating these features in a random and pre-determined manner are developed and tested. All the
selection schemes exhibit good convergence characteristics, in particular the one in which both the
size of the sub-community and the pair of the mates in the sub-community are determined randomly.
The efficiency of the proposed selection operator is shown by applying the method for the optimal
design of three well-known benchmark networks, namely two-loop, Hanoi and New-York networks.
The proposed scheme produces results comparable to the best results presented in the literature
with much less computational effort
M.h. Afshar, A. Afshar, M. A. Mariño, Hon. M. Asce,
Volume 7, Issue 2 (6-2009)
Abstract
This paper presents the application of an iterative penalty method for the design of water distribution pipe
networks. The optimal design of pipe networks is first recasted into an unconstrained minimization problem via the use
of the penalty method, which is then solved by a global mathematical optimization tool. The difficulty of using a trial
and error procedure to select the proper value of the penalty parameter is overcome by an iterative use of the penalty
parameter. The proposed method reduces the original problem with a priori unknown penalty parameter to a series of
similar optimization problems with known and increasing value of the penalty parameters. An iterative use of the
penalty parameter is then implemented and its effect on the final solution is investigated. Two different methods of
fitting, namely least squares and cubic splines, are used to continuously approximate the discrete pipe cost function
and are tested by numerical examples. The method is applied to some benchmark examples and the results are
compared with other global optimization approaches. The proposed method is shown to be comparable to existing
global optimization methods.
