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

Area of Science:

• Environmental Engineering
• Computational Science
• Optimization Techniques

Background:

• Heuristic optimization is increasingly used in environmental engineering.
• Algorithm selection and configuration are often performed without a systematic approach.

Purpose of the Study:

• To evaluate algorithm-tuning procedures for global optimization techniques.
• To benchmark these procedures using a multilayer sorptive barrier design problem.

Main Methods:

• Applied genetic algorithms, simulated annealing, and particle swarm optimization.
• Configured the design problem as a combinatorial optimization task.
• Focused on minimizing the transport of organic contaminants in landfill liners.

Main Results:

• The binary-coded genetic algorithm demonstrated high efficiency with minimal tuning.
• Careful integration of constraint violations is crucial for effective algorithm convergence.
• Contaminant properties significantly influence search algorithm performance.

Conclusions:

• Formal algorithm tuning can substantially enhance performance in environmental engineering optimization.
• This systematic approach offers insights into the physical processes governing environmental systems.