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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Published on: December 9, 2012

Multi-objective optimization with controlled model assisted evolution strategies.

Jan Braun1, Johannes Krettek, Frank Hoffmann

  • 1Institute for Control and Systems Engineering, TU Dortmund, Dortmund, 44221, Germany. jan.braun@tu-dortmund.de

Evolutionary Computation
|November 18, 2009
PubMed
Summary
This summary is machine-generated.

Model-assisted evolutionary algorithms improve convergence by using a data-based model for preselection, reducing expensive fitness evaluations. This approach enhances multi-objective optimization by adapting selection pressure to model quality, outperforming standard strategies.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Evolutionary algorithms (EAs) excel in complex search spaces but demand numerous fitness evaluations, limiting their application in real-time optimization and simulations.
  • Standard EAs lack memory of past evaluations, storing only current generation solutions, which hinders efficient learning and adaptation.
  • Model-assisted EAs leverage historical data to build predictive models, enabling informed selection decisions and reducing computational cost.

Purpose of the Study:

  • To introduce a novel model-assisted evolutionary algorithm scheme for enhanced optimization performance.
  • To extend model-assisted optimization from scalar to multi-objective problems.
  • To investigate the impact of adaptive preselection pressure controlled by model quality on convergence.

Main Methods:

  • Development of an instance-based fitness model for preselecting solutions.
  • Implementation of a lambda-control mechanism to dynamically adjust selection pressure based on model quality.
  • Extension of model-assisted concepts to multi-objective optimization through redefined model quality and pressure control.

Main Results:

  • The proposed model-assisted strategy demonstrated superior convergence compared to a standard multi-objective evolutionary algorithm on benchmark problems.
  • Regulated preselection, guided by model quality, proved more effective than static preselection in improving optimization efficiency.
  • The lambda-control effectively adapted the number of true fitness evaluations based on the monitored model's accuracy.

Conclusions:

  • Model-assisted evolutionary algorithms offer a significant advantage in computational efficiency and convergence speed for complex optimization tasks.
  • The adaptive lambda-control mechanism is crucial for optimizing the balance between model-based and true fitness evaluations.
  • This approach effectively extends the benefits of model-assisted optimization to the challenging domain of multi-objective problems.