A parameter-free self-adapting boundary genetic search for pipe network optimization |
| |
Authors: | M H Afshar M A Mariño |
| |
Institution: | (1) Dept. of Civil Engineering, Iran Univ. of Science and Tech., Narmak, Tehran, Iran, 16844;(2) Dept. of Land, Air and Water Resources and Dept. of Civil and Environmental Engineering, University of California, 95616 Davis, CA, USA |
| |
Abstract: | Commercial application of genetic algorithms (GAs) to engineering design problems, including optimal design of pipe networks,
could be facilitated by the development of algorithms that require the least number of parameter tuning. This paper presents
an attempt to eliminate the need for defining a priori the proper penalty parameter in GA search for pipe networks optimal
designs. The method is based on the assumption that the optimal solution of a pipe network design problem lies somewhere on,
or near, the boundary of the feasible region. The proposed method uses the ratio of the best feasible and infeasible designs
at each generation to guide the direction of the search towards the boundary of the feasible domain by automatically adjusting
the value of the penalty parameter. The value of the ratio greater than unity is interpreted as the search being performed
in the feasible region and vice versa. The new adapted value of the penalty parameter at each generation is therefore calculated
as the product of its current value and the aforementioned ratio. The genetic search so constructed is shown to converge to
the boundary of the feasible region irrespective of the starting value of the constraint violation penalty parameter. The
proposed method is described here in the context of pipe network optimisation problems but is equally applicable to any other
constrained optimisation problem. The effectiveness of the method is illustrated with a benchmark pipe network optimization
example from the literature. |
| |
Keywords: | Self-adaptive Boundary search Pipe networks Optimal design Genetic algorithm |
本文献已被 SpringerLink 等数据库收录! |
|