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    Existing dynamic multiobjective optimization test problems have biases. This study introduces a new, scalable test suite that better evaluates algorithms in dynamic environments.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Optimization

    Background:

    • Dynamic multiobjective optimization (DMO) is crucial for algorithms adapting to changing environments.
    • Existing DMO test problems often lack rigorous analysis, introducing biases.
    • Current test problems overemphasize static features, hindering accurate algorithm evaluation.

    Purpose of the Study:

    • To identify biases in current dynamic multiobjective optimization test problems.
    • To define essential characteristics for a robust DMO test suite.
    • To develop a novel, scalable continuous test suite for DMO algorithms.

    Main Methods:

    • Review and analysis of widely used dynamic multiobjective test problems.
    • Identification of limitations including poor scalability and overemphasis on static properties.
    • Development of a new scalable continuous test suite incorporating diverse and underrepresented dynamics.

    Main Results:

    • Identified significant biases in existing DMO test problems.
    • Proposed a set of desirable dynamics and features for effective test suites.
    • Empirical studies confirmed the proposed test suite is more challenging and comprehensive.

    Conclusions:

    • The developed test suite addresses limitations of existing DMO benchmarks.
    • It offers a more rigorous evaluation of algorithms' dynamic adaptation capabilities.
    • This new suite facilitates more accurate conclusions on algorithm performance in dynamic settings.