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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary

Miqing Li1, Xin Yao2

  • 1CERCIA, School of Computer Science, University of Birmingham, Birmingham B15 2TT, U.K. limitsing@gmail.com.

Evolutionary Computation
|February 27, 2020
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Summary

This study introduces AdaW, an adaptive weight strategy for evolutionary multi-objective optimization (EMO) algorithms. AdaW effectively optimizes solutions across diverse Pareto front shapes without prior problem knowledge.

Keywords:
Multiobjective optimisationdecomposition-based EMOevolutionary algorithmsmany-objective optimisationweight adaptation.

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Area of Science:

  • Multi-objective optimization
  • Evolutionary computation
  • Algorithm design

Background:

  • Decomposition-based evolutionary multi-objective optimization (EMO) algorithms' performance relies on matching weight distributions to Pareto front shapes.
  • Uniform weight distributions are effective for simplex-like fronts but fail for others.
  • Specifying appropriate weights without prior Pareto front information remains a challenge.

Purpose of the Study:

  • To propose an adaptive weight strategy (AdaW) for EMO algorithms.
  • To enable effective weight adaptation during the evolutionary process.
  • To address the challenge of weight specification for diverse Pareto front shapes.

Main Methods:

  • Developed AdaW, an approach for adaptive weight strategy in EMO.
  • Incorporated weight generation, addition, deletion, and update frequency into the adaptation process.
  • Tested AdaW across various Pareto front shapes.

Main Results:

  • AdaW demonstrated effectiveness in generating quality solution sets.
  • The approach successfully handled simplex-like, inverted simplex-like, highly nonlinear, disconnected, degenerate, scaled, and high-dimensional Pareto fronts.
  • Experimental results confirmed the proposed approach's efficacy.

Conclusions:

  • AdaW provides a robust solution for weight adaptation in EMO.
  • The method enhances solution quality across a wide range of Pareto front complexities.
  • AdaW overcomes limitations of fixed weight distributions in EMO algorithms.