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    Area of Science:

    • Control Theory
    • Distributed Systems
    • Optimization

    Background:

    • Network-connected multiagent systems require efficient resource allocation.
    • Distributed decision-making presents challenges in coordinating agents with local preferences.

    Purpose of the Study:

    • To formulate and solve a distributed multiobjective optimization problem for resource allocation.
    • To develop novel distributed algorithms for network-connected multiagent systems.
    • To ensure robust Pareto solutions and data privacy.

    Main Methods:

    • Utilizing a weighted Lp preference index for robust Pareto solutions.
    • Employing distributed fixed-time optimization for online construction of the Lp preference index.
    • Applying consensus and gradient descent techniques with Lyapunov theories for convergence.

    Main Results:

    • A unique Pareto solution is proven to exist for the formulated problem.
    • Asymptotic convergence to the optimal solution is established.
    • The proposed algorithms effectively allocate resources in a distributed manner.

    Conclusions:

    • The developed distributed algorithms offer an effective solution for resource allocation in multiagent systems.
    • The framework ensures data privacy and does not require prior information.
    • Numerical validation confirms the effectiveness of the proposed methods.