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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

A preference-based evolutionary algorithm for multi-objective optimization.

Lothar Thiele1, Kaisa Miettinen, Pekka J Korhonen

  • 1ETH-Zurich, Department of Information Technology and Electrical Engineering, Gloriastrasse 35, CH-8092 Zürich, Switzerland. thiele@tik.ee.ethz.ch

Evolutionary Computation
|August 28, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a preference-based evolutionary algorithm for multi-objective optimization. It guides the search towards preferred solutions using decision-maker aspirations, improving efficiency.

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Area of Science:

  • Computational Intelligence
  • Operations Research
  • Multi-objective Optimization

Background:

  • Evolutionary multi-objective optimization (EMO) often generates the entire Pareto optimal set.
  • Decision-makers may have specific preferences not fully captured by traditional EMO methods.
  • Interactive algorithms require efficient ways to incorporate user preferences.

Purpose of the Study:

  • To propose a preference-based evolutionary approach for multi-objective optimization.
  • To integrate decision-maker preference information into an interactive evolutionary algorithm.
  • To improve the efficiency of finding preferred solutions within the Pareto optimal set.

Main Methods:

  • An interactive evolutionary algorithm is proposed.
  • Decision-makers provide preference information via a reference point (aspiration levels).
  • A new population is generated by combining a fitness function and an achievement scalarizing function.

Main Results:

  • The approach concentrates the search towards areas with more preferred alternatives.
  • It avoids generating the entire Pareto optimal set with equal accuracy.
  • Numerical examples demonstrate the effectiveness of the proposed method.

Conclusions:

  • Preference information can be effectively incorporated into evolutionary multi-objective optimization.
  • The proposed interactive approach enhances the efficiency of finding desired solutions.
  • This method offers a more focused search for decision-makers in multi-objective problems.