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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimum tracking with evolution strategies.

Dirk V Arnold1, Hans-Georg Beyer

  • 1Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada B3H 1W5. dirk@cs.dal.ca

Evolutionary Computation
|August 15, 2006
PubMed
Summary

This study analyzes evolutionary algorithms for dynamic optimization. Cumulative step length adaptation performs suboptimally when the target moves linearly, unlike in random movement scenarios.

Area of Science:

  • Computational Intelligence
  • Optimization Theory
  • Evolutionary Computation

Background:

  • Dynamic optimization problems (DOPs) require algorithms that adapt to changing objectives.
  • Evolutionary algorithms (EAs) are often used for DOPs, but their performance depends on strategy parameters and operators.
  • Understanding EA behavior in simplified dynamic environments aids in designing better algorithms.

Purpose of the Study:

  • To investigate the tracking performance of a multiparent evolution strategy with cumulative step length adaptation.
  • To analyze the strategy's behavior in a specific dynamic environment: a linearly moving target.
  • To derive scaling laws for better understanding and prediction of the strategy's performance.

Main Methods:

  • Mathematical analysis of a multiparent evolution strategy.

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  • Simulation in an idealized environment with a linearly moving target.
  • Derivation of scaling laws to describe strategy behavior.

    • Main Results:

    • Accurate scaling laws were derived, enhancing the understanding of the strategy's performance.
    • Cumulative step length adaptation was found to be suboptimal for linearly moving targets.
    • This contrasts with previous findings for randomly moving targets, where optimal step lengths were achieved.

    Conclusions:

    • Cumulative step length adaptation is not universally optimal for all types of target movement in dynamic optimization.
    • The findings suggest that specific parameter choices, like population size, may need re-evaluation for linear target movement.
    • Further research into EA parameter tuning for different dynamic environments is warranted.