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http://dx.doi.org/10.14249/eia.2013.22.6.713

Application of multi-objective genetic algorithm for waste load allocation in a river basin  

Cho, Jae-Heon (Department of Health and Environment, Kwandong University)
Publication Information
Journal of Environmental Impact Assessment / v.22, no.6, 2013 , pp. 713-724 More about this Journal
Abstract
In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.
Keywords
waste load allocation; Gini coefficient; multi-objective genetic algorithm; sensitivity analysis; spacing;
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Times Cited By KSCI : 4  (Citation Analysis)
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