Genetic algorithm for constrained optimization models and its application in groundwater resources management

Date

2008-01-01

Author

Guan, Jiabao
Kentel Erdoğan, Elçin
Aral, Mustafa M.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

67
views

0
downloads

Genetic algorithms (GAs) have been shown to be an efficient tool for the solution of unconstrained optimization problems. In their standard form, GA formulations are "blind" to the constraints of an optimization model when the model involves these constraints. Thus, in GA applications alternative procedures are used to satisfy the constraints of the optimization model. In this study, the method that is utilized in the Complex Algorithm to solve constrained optimization problems is abstracted to develop a repairing procedure for GAs. The proposed procedure, which handles infeasible solutions that may be generated in a standard GA process, is embedded into the conventional GA to yield an improved GA process (IGA) for the solution of optimization problems with equality and inequality constrains. Two numerical examples are included to demonstrate the effectiveness and efficiency of the proposed method for the solution of constrained optimization applications. Finally the IGA is successfully used to develop an optimal groundwater management plan for the Savannah, Ga. region.

Subject Keywords

Geography, Planning and Development, Civil and Structural Engineering, Management, Monitoring, Policy and Law, Water Science and Technology

URI

https://hdl.handle.net/11511/40079

Journal

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE

DOI

https://doi.org/10.1061/(asce)0733-9496(2008)134:1(64)

Collections

Department of Civil Engineering, Article

Suggestions

OpenMETU
Core

Impacts of physical and chemical heterogeneities on aquifer remediation design Aksoy, Ayşegül (American Society of Civil Engineers (ASCE), 2004-07-01) The impacts of physical and chemical aquifer heterogeneities on optimal remediation design, costs, and time to compliance are investigated by linking a genetic algorithm with a contaminant transport simulation model. Physical and chemical aquifer heterogeneities were grouped into three levels as follows: (1) hydraulic conductivity (K) heterogeneity only; (2) combined heterogeneity of K and the distribution coefficient (Kd); and (3) combined heterogeneity of K, Kd, and the mass transfer rate (α). Various deg...
Identification of the Best Booster Station Network for a Water Distribution System AYVAZ, MUSTAFA TAMER; Kentel Erdoğan, Elçin (American Society of Civil Engineers (ASCE), 2015-05-01) A fuzzy decision-making framework (DMF) is combined with a hybrid genetic algorithm-linear programming (GA-LP) optimization approach to determine the best booster station network for a water distribution system. The proposed hybrid GA-LP model simultaneously optimizes two conflicting objectives; namely, minimization of total chlorine injection dosage and the number of booster stations. At the same time, residual chlorine concentrations are kept within desired limits. Adjustment of the relative importance of...
Calibration and verification of QUAL2E using Genetic algorithm optimization GÖKTAŞ, Recep Kaya; Aksoy, Ayşegül (American Society of Civil Engineers (ASCE), 2007-03-01) In this study, the QUAL2E model is linked with a genetic algorithm (GA) in order to conduct the calibration and verification of the model. The efficacy of the optimization model was tested for different observation data quality represented by the perfect and noisy data assumptions. Four cases were studied. In the first (base) case, calibration was conducted for the reach-variable reaeration coefficient (K2) and the sediment oxygen demand rate (K4). In the second case, the number of sampling points was incre...
Genetic Algorithm Application to the Structural Properties of Si-Ge Mixed Clusters Dugan, Nazim; Erkoç, Şakir (Informa UK Limited, 2009-01-01) Optimum geometries of silicon-germanium (Si-Ge) clusters are found using a single parent genetic algorithm. 100 atom and 150 atom clusters are studied with some variety of compositions and initial geometries. Total interaction energies, distances of Si and Ge atoms to the cluster centers, and average bond lengths are calculated. Si-core Ge-shell geometry is found to be favorable compared to other geometries.
Optimal load and resistance factor design of geometrically nonlinear steel space frames via tabu search and genetic algorithm DEĞERTEKİN, SADIK ÖZGÜR; Saka, M. P.; HAYALİOĞLU, MEHMET SEDAT (Elsevier BV, 2008-01-01) In this paper, algorithms are presented for the optimum design of geometrically nonlinear steel space frames using tabu search and genetic algorithm. Tabu search utilizes the features of short-term memory facility (tabu list) and aspiration criteria. Genetic algorithm employs reproduction, crossover and mutation operators. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange...

Citation Formats

J. Guan, E. Kentel Erdoğan, and M. M. Aral, “Genetic algorithm for constrained optimization models and its application in groundwater resources management,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE, pp. 64–72, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40079.