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    This study introduces a novel benchmark generator for dynamic multiobjective optimization problems (DMOPs), addressing complex, time-varying challenges. The research evaluates algorithms using new performance measures, enhancing understanding of their capabilities in dynamic optimization.

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    Area of Science:

    • Optimization
    • Computational Intelligence
    • Algorithmics

    Background:

    • Real-world optimization problems often involve multiple conflicting objectives that evolve over time, termed dynamic multiobjective optimization problems (DMOPs).
    • Existing evolutionary algorithms face significant challenges in addressing the time-varying nature of DMOPs.
    • A lack of diverse and representative benchmark functions hinders the thorough evaluation of algorithms for DMOPs.

    Purpose of the Study:

    • To propose a new benchmark generator for dynamic multiobjective optimization problems (DMOPs).
    • To incorporate challenging characteristics like mixed Pareto-optimal fronts, nonmonotonic and time-varying variable linkages, and mixed types of changes with randomness.
    • To introduce novel performance measures for evaluating algorithms on DMOPs with diverse dynamic features.

    Main Methods:

    • Development of a versatile benchmark generator for DMOPs.
    • Creation of a test suite comprising ten instances with varied dynamic features.
    • Proposal of new performance metrics tailored for DMOP evaluation.
    • Empirical investigation of six representative multiobjective evolutionary algorithms.

    Main Results:

    • A new benchmark generator capable of tuning complex DMOP characteristics was successfully developed.
    • A test suite of ten diverse DMOP instances and new performance measures were introduced.
    • Experimental results provided insights into the strengths and weaknesses of compared algorithms on the proposed test suite.
    • The study facilitated a better understanding of algorithm performance in dynamic optimization scenarios.

    Conclusions:

    • The proposed benchmark generator and performance measures offer a valuable resource for advancing research in dynamic multiobjective optimization.
    • The comprehensive evaluation provides critical insights into the effectiveness of existing algorithms for DMOPs.
    • This work contributes to the development of more robust and adaptive evolutionary algorithms for time-varying optimization challenges.