Enhancements to Genetic Algorithm for Optimal Ground-Water Management
Publication: Journal of Hydrologic Engineering
Volume 5, Issue 1
Abstract
Genetic algorithm (GA) is considered to be a robust technique for solving ground-water optimization problems. Often these problems are difficult to solve using traditional gradient-based techniques as these are nonlinear, nonconvex, and discontinuous. In this manuscript, recent research related to application of GA in solving these problems is critically reviewed, and three areas of potential enhancement to GA are identified and explored. These enhancement methods to GA are fitness reduction method (FRM), search bound sampling method (SBSM), and optimal resource allocation guideline (ORAG). In order to assess these methods, a nonlinear ground-water problem with fixed and variable costs is selected (from literature) where the corresponding optimal solution using a gradient-based nonlinear programming (NLP) technique is available. The problem is resolved using GA coupled with the enhancement methods, and the GA solutions are compared with the NLP solution. In addition, the sensitivity of these methods to various GA parameters are studied. The results of the analysis using the enhancement methods show the following: (1) FRM enhances efficiency of GA in handling constraints; (2) SBSM enhances accuracy of GA in solving problems with fixed costs; (3) ORAG enhances reliability of GA by providing some convergence guarantee for a given computational resource; and (4) when applied with FRM and SBSM, accuracy of GA is marginally increased from near-optimal to global-optimal by tuning any one of the several parameters. There appears to be little need to embark on a full-scale analysis for achieving the increase.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Aarts, E., and Korst, J. (1989). Simulated annealing and Boltzman machines. Wiley, New York.
2.
Carrera, J., Usunoff, E., and Szidarovsky, F. (1984). “A method for optimal observation network design for groundwater management.” J. Hydrol., 73, 147–163.
3.
Cieniawski, S. E., Eheart, J. W., and Ranjithan, S. (1995). “Using genetic algorithms to solve a multiobjective groundwater monitoring problem.” Water Resour. Res., 31(2), 339–409.
4.
DeJong, K. A. ( 1975). “An analysis of the behavior of a class of genetic adaptive systems,” PhD dissertation, University of Michigan, Ann Arbor, Mich.
5.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, Mass.
6.
Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, Mich.
7.
Huang, C., and Mayer, A. S. ( 1995). “Dynamic optimal control for groundwater remediation management in using genetic algorithms.” Models for accessing and monitoring groundwater quality, Proc., of a Boulder Symposium, IAHS Publication 227, 149–155.
8.
Johnson, V. M., and Rogers, L. L. (1995). “Location analysis in groundwater remediation using neural networks.” Ground Water, 33(5), 749–758.
9.
McKinney, D. C., and Lin, M. D. (1994). “Genetic algorithm solution to groundwater management models.” Water Resour. Res., 30(6), 1897–1906.
10.
Michalewicz, Z. (1992). Genetic algorithm + data structure = evolution programs. Springer, New York.
11.
Morgan, D. R., Eheart, J. W., and Valocchi, A. J. (1993). “Aquifer remediation design under uncertainty using a new chance constrained programming technique.” Water Resour. Res., 29(3), 551–561.
12.
Ritzel, B. J., Eheart, J. W., and Ranjithan, S. (1994). “Using genetic algorithm to solve a multiple objective groundwater pollution containment problem.” Water Resour. Res., 30(5), 1589–1603.
13.
Rogers, L. L., and Dowla, F. U. (1994). “Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling.” Water Resour. Res., 30(2), 457–481.
14.
Rogers, L. L., Dowla, F. U., and Johnson, V. M. (1995). “Optimal field-scale groundwater remediation using neural networks and the genetic algorithm.” Envir. Sci. and Technol., 29(5), 1145–1155.
15.
Wagner, B. J. (1995). “Sampling design methods for groundwater modeling under uncertainty.” Water Resour. Res., 31(10), 2581–2591.
16.
Willis, R. L., and Yeh, W. W.-G. (1987). Groundwater systems planning and management. Prentice-Hall, Englewood Cliffs, N.J.
Information & Authors
Information
Published In
History
Received: May 10, 1997
Published online: Jan 1, 2000
Published in print: Jan 2000
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.