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    A new multistate-constrained multiobjective differential evolution (DE) algorithm with variable neighborhood strategy (MSCMODE-VNS) improves optimization efficiency for complex feasible regions. This enhanced DE approach accelerates feasible solution generation and improves population performance.

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    Area of Science:

    • Optimization Algorithms
    • Computational Intelligence
    • Operations Research

    Background:

    • Multiobjective differential evolution (DE) algorithms are widely applied but struggle with complex feasible regions in constrained multiobjective problems.
    • Low efficiency and difficulty in promoting population evolution arise from a scarcity of feasible solutions in challenging optimization landscapes.

    Purpose of the Study:

    • To propose a novel multistate-constrained multiobjective DE with variable neighborhood strategy (MSCMODE-VNS) to enhance optimization effectiveness.
    • To address the challenges posed by complex feasible regions in constrained multiobjective optimization problems.

    Main Methods:

    • A variable neighborhood DE strategy, incorporating a specialized convergence indicator, is employed to expedite the generation of feasible solutions.
    • A multistate population updating strategy with a comprehensive solution evaluation mechanism is developed for next-generation population improvement.
    • Convergence analysis using probability theory is conducted to validate the algorithm's effectiveness.

    Main Results:

    • The proposed MSCMODE-VNS algorithm demonstrates enhanced optimization effectiveness on problems with complex feasible regions.
    • Experimental results show satisfactory performance across three benchmark test suites and two real-world constrained multiobjective problems.

    Conclusions:

    • The MSCMODE-VNS algorithm effectively overcomes the limitations of traditional MODE in handling complex feasible regions.
    • The study validates the algorithm's capability to improve optimization efficiency and solution performance in constrained multiobjective problems.