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A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms.

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Summary

This study classifies multiobjective evolutionary algorithms based on their integration with surrogate models, offering a new perspective for computationally expensive optimization problems. It identifies advantages and disadvantages of different integration approaches.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Machine learning

Background:

  • Multiobjective optimization problems (MOPs) are computationally expensive.
  • Surrogate models are used to reduce the number of evaluations needed to approximate the Pareto front.
  • Existing reviews focus on surrogate model types, not their integration with algorithms.

Purpose of the Study:

  • To classify multiobjective evolutionary algorithms (MOEAs) based on their integration with surrogate models.
  • To provide a novel classification scheme for surrogate-assisted MOEAs.
  • To identify advantages and disadvantages of different integration strategies.

Main Methods:

  • Review and classification of existing literature on surrogate-assisted MOEAs.
  • Categorization based on the interaction and integration of surrogate models within MOEAs.
  • Analysis of identified classes to determine pros and cons.

Main Results:

  • A new classification framework for surrogate-assisted MOEAs is proposed.
  • Similar approaches are grouped based on their integration patterns.
  • Key advantages and disadvantages of each integration class are identified.

Conclusions:

  • The proposed classification offers a new perspective on surrogate-assisted MOEAs.
  • Understanding integration strategies is crucial for efficient optimization of expensive problems.
  • This work provides a foundation for future research in surrogate-assisted evolutionary optimization.