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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Tianyu Liu1, Xiangfei Wu2, He Xu3

  • 1School of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China liuty@shmtu.edu.cn.

Evolutionary Computation
|March 4, 2026
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Summary
This summary is machine-generated.

This study introduces DS-DMOEA, an advanced algorithm for dynamic multi-objective optimization problems. It effectively tracks Pareto-optimal solutions in changing environments using dual-space prediction and surrogate-based sampling.

Keywords:
Dynamic multi-objective evolutionary algorithmdual-space predictionsurrogate-based sampling

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Area of Science:

  • Optimization
  • Evolutionary Computation
  • Artificial Intelligence

Background:

  • Dynamic multi-objective optimization problems (DMOEPs) require algorithms to track Pareto-optimal solutions amidst environmental changes.
  • Existing prediction-based dynamic multi-objective evolutionary algorithms (DMOEAs) often use single-space prediction or identical models for both decision and objective spaces, limiting effectiveness in complex dynamics.
  • Sampling methods in DMOEAs can lead to significant computational burdens due to excessive function evaluations.

Purpose of the Study:

  • To propose a novel dynamic multi-objective evolutionary algorithm (DS-DMOEA) that efficiently adapts to environmental changes.
  • To address the limitations of existing DMOEAs in capturing distinct space dynamics and managing computational load.

Main Methods:

  • DS-DMOEA employs a dual-space prediction strategy: a weight vector-based method for the objective space and a geodesic flow kernel method for the decision space.
  • A surrogate-based sampling strategy is utilized to generate high-quality initial populations for new environments by training surrogate models on historical data.
  • The predicted and sampled populations are combined to form an optimized initial population for the evolving environment.

Main Results:

  • DS-DMOEA was rigorously tested against nine state-of-the-art DMOEAs on 19 benchmark problems.
  • The algorithm demonstrated effectiveness across three distinct environmental change patterns.
  • Experimental results validated the superior performance of the proposed DS-DMOEA.

Conclusions:

  • The proposed DS-DMOEA effectively adapts to dynamic multi-objective optimization problems through its dual-space prediction and surrogate-based sampling strategies.
  • The algorithm overcomes limitations of existing methods by capturing complex dynamics in both decision and objective spaces while managing computational cost.
  • DS-DMOEA represents a significant advancement in efficiently handling dynamic multi-objective optimization challenges.