Constraint-handling methods for optimal groundwater remediation design by genetic algorithms

Typically in optimal groundwater remediation design, the objective is to minimize the cost of remediation while meeting the water quality constraints by the end of the remediation period. Given that many common groundwater contaminants are hazardous at very low concentrations, even a small violation of the water quality may be the difference between reaching a hazardous or nonhazardous end point. Furthermore, the remediation costs increase dramatically as one attempts to remove the last units of concentration. This work compares two methods for constraint-handling, an additive penalty method and a multiplicative penalty, for use in optimal groundwater remediation design with a genetic algorithm. The multiplicative approach was found to be a more robust technique for finding cost-effective designs, while enforcing the water quality constraints