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    We introduce edge-cloud co-evolutionary algorithms (ECCoEAs) to address distributed data-driven optimization challenges in the Internet of Things. ECCoEAs effectively solve problems with data distributed across edge servers and the cloud.

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    Area of Science:

    • Distributed Optimization
    • Artificial Intelligence
    • Internet of Things

    Background:

    • Surrogate-assisted evolutionary algorithms (EAs) are effective for data-driven optimization but typically centralized.
    • Existing methods do not address challenges of distributed data in edge computing environments.

    Purpose of the Study:

    • To propose a novel framework, edge-cloud co-EAs (ECCoEAs), for solving distributed data-driven optimization problems.
    • To develop a distributed framework enabling collaboration between edge and cloud servers.

    Main Methods:

    • A distributed framework comprising a communication mechanism, edge model management, and cloud model management.
    • Edge models trained on local data and co-evolved solutions; cloud models guide edge management using predictions.
    • Implementation of two ECCoEAs and design of a novel benchmark test suite for distributed optimization.

    Main Results:

    • The proposed communication mechanism prevents deadlocks in edge-cloud collaboration.
    • ECCoEAs demonstrate generality and effectiveness on benchmark and practical distributed clustering problems.
    • Validation of the framework's performance in distributed data-driven optimization scenarios.

    Conclusions:

    • ECCoEAs provide an effective solution for distributed data-driven optimization problems in edge computing.
    • The framework enhances optimization capabilities by leveraging edge and cloud resources collaboratively.
    • The study validates the practical applicability and effectiveness of ECCoEAs.