Application of heuristic optimization techniques and algorithm tuning to multilayered sorptive barrier design

L. Shawn Matott, Shannon L. Bartelt-Hunt, Alan J. Rabideau, K. R. Fowler

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)6354-6360
Number of pages7
JournalEnvironmental Science and Technology
Volume40
Issue number20
DOIs
StatePublished - Oct 15 2006

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry

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