期刊
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE
卷 131, 期 1, 页码 14-24出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)0733-9496(2005)131:1(14)
关键词
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Optimal groundwater remediation design problems are often complex, nonlinear, and computationally intensive. Genetic algorithms allow solution of more complex nonlinear problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). This paper presents a new self-adaptive HGA (SAHGA) and compares its performance to a nonadaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on a groundwater remediation problem. Of the two hybrid algorithms, SAHGA is shown to be far more robust than NAHGA, providing fast convergence across a broad range of parameter settings. For the test problem, SAHGA needs 75% fewer function evaluations than SGA, even with an inefficient local search method. These findings demonstrate that SAHGA has substantial promise for enabling solution of larger-scale problems than was previously possible.
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