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    Area of Science:

    • Control Systems
    • Optimization Theory
    • Distributed Computing

    Background:

    • Distributed optimization problems often involve complex, coupled constraints.
    • Multiagent systems (MASs) offer a framework for decentralized problem-solving.
    • Bilevel optimization presents unique challenges due to its hierarchical structure.

    Purpose of the Study:

    • To develop novel multiagent systems (MASs) for distributed bilevel constrained optimization.
    • To address the computational demands and constraint complexities inherent in these problems.
    • To ensure cooperative optimization among agents while adhering to global and local constraints.

    Main Methods:

    • Customization of first and second-order multiagent systems (MASs).
    • Development of a distributed bilevel optimization framework with a summation objective function.
    • Implementation of cooperative optimization strategies for local objective functions.

    Main Results:

    • The proposed MASs are proven to converge to the optimal solution.
    • Demonstrated enhanced robustness against communication disruptions.
    • Verified fast convergence through numerical simulations and an economic dispatch problem.

    Conclusions:

    • The developed MASs effectively solve distributed bilevel constrained optimization problems.
    • The approaches offer a robust and efficient solution for complex optimization tasks.
    • The findings have implications for decentralized control and resource allocation in networked systems.