TY - JOUR
T1 - Application of heuristic optimization techniques and algorithm tuning to multilayered sorptive barrier design
AU - Matott, L. Shawn
AU - Bartelt-Hunt, Shannon L.
AU - Rabideau, Alan J.
AU - Fowler, K. R.
PY - 2006/10/15
Y1 - 2006/10/15
N2 - Although heuristic optimization techniques are increasingly applied in environmental engineering applications, algorithm selection and configuration are often approached in an ad hoc fashion. In this study, the design of a multilayer sorptive barrier system served as a benchmark problem for evaluating several algorithm-tuning procedures, as applied to three global optimization techniques (genetic algorithms, simulated annealing, and particle swarm optimization). Each design problem was configured as a combinatorial optimization in which sorptive materials were selected for inclusion in a landfill liner to minimize the transport of three common organic contaminants. Relative to multilayer sorptive barrier design, study results indicate (i) the binary-coded genetic algorithm is highly efficient and requires minimal tuning, (ii) constraint violations must be carefully integrated to avoid poor algorithm convergence, and (iii) search algorithm performance is strongly influenced by the physical-chemical properties of the organic contaminants of concern. More generally, the results suggest that formal algorithm tuning, which has not been widely applied to environmental engineering optimization, can significantly improve algorithm performance and provide insight into the physical processes that control environmental systems.
AB - Although heuristic optimization techniques are increasingly applied in environmental engineering applications, algorithm selection and configuration are often approached in an ad hoc fashion. In this study, the design of a multilayer sorptive barrier system served as a benchmark problem for evaluating several algorithm-tuning procedures, as applied to three global optimization techniques (genetic algorithms, simulated annealing, and particle swarm optimization). Each design problem was configured as a combinatorial optimization in which sorptive materials were selected for inclusion in a landfill liner to minimize the transport of three common organic contaminants. Relative to multilayer sorptive barrier design, study results indicate (i) the binary-coded genetic algorithm is highly efficient and requires minimal tuning, (ii) constraint violations must be carefully integrated to avoid poor algorithm convergence, and (iii) search algorithm performance is strongly influenced by the physical-chemical properties of the organic contaminants of concern. More generally, the results suggest that formal algorithm tuning, which has not been widely applied to environmental engineering optimization, can significantly improve algorithm performance and provide insight into the physical processes that control environmental systems.
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U2 - 10.1021/es052560+
DO - 10.1021/es052560+
M3 - Article
C2 - 17120565
AN - SCOPUS:33750307951
SN - 0013-936X
VL - 40
SP - 6354
EP - 6360
JO - Environmental Science and Technology
JF - Environmental Science and Technology
IS - 20
ER -