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Distributed Differential Evolution With Adaptive Resource Allocation.

Jian-Yu Li, Ke-Jing Du, Zhi-Hui Zhan

    IEEE Transactions on Cybernetics
    |March 14, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a novel Distributed Differential Evolution with Adaptive Resource Allocation (DDE-ARA) framework. It efficiently reallocates fitness evaluations from underperforming to well-performing populations, enhancing optimization performance.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Distributed Computing

    Background:

    • Distributed Differential Evolution (DDE) utilizes multiple populations for complex optimization.
    • Fitness evaluation (FE) budget allocation significantly impacts DDE performance.
    • Existing DDE methods lack adaptive resource management strategies.

    Purpose of the Study:

    • To propose a novel three-layer DDE framework with adaptive resource allocation (DDE-ARA).
    • To enhance the optimization ability and efficiency of DDE algorithms.
    • To introduce methods for performance measurement and adaptive FE allocation.

    Main Methods:

    • Developed a three-layer DDE-ARA framework: algorithm, dispatch, and machine layers.
    • Proposed a General Performance Indicator (GPI) to assess DE population performance.
    • Introduced a Fitness Evaluation Allocation (FEA) method for adaptive resource redistribution.
    • Implemented a load balance strategy in the dispatch layer for improved efficiency.

    Main Results:

    • DDE-ARA demonstrated superior effectiveness and efficiency across 30 CEC 2014 benchmark functions.
    • The proposed GPI and FEA methods significantly improved search efficiency.
    • The load balance strategy enhanced overall algorithm speedup.
    • Theoretical analysis confirmed the framework's effectiveness and provided an optimization error lower bound.

    Conclusions:

    • The DDE-ARA framework offers a significant advancement in distributed optimization.
    • Adaptive resource allocation is crucial for optimizing DDE performance.
    • The proposed methods provide a robust and efficient approach to complex problem-solving.