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    Area of Science:

    • Evolutionary Computation
    • Machine Learning
    • Optimization Algorithms

    Background:

    • Large-scale optimization presents significant challenges in evolutionary computation, including slow convergence and entrapment in local optima.
    • Existing algorithms struggle with the vast search spaces and numerous suboptima inherent in large-scale problems.
    • Addressing these issues is crucial for advancing the field of computational intelligence.

    Purpose of the Study:

    • To propose a novel algorithm, adaptive granularity learning distributed particle swarm optimization (AGLDPSO), to tackle the dual challenges of slow convergence and local optima in large-scale optimization.
    • To leverage machine learning techniques, specifically locality-sensitive hashing (LSH) for clustering and logistic regression (LR) for adaptive control, within a distributed particle swarm optimization framework.
    • To enhance the performance of evolutionary computation algorithms on complex, large-scale optimization tasks.

    Main Methods:

    • Implemented a master-slave, multi-subpopulation distributed model where populations co-evolve, facilitating information exchange and enhancing diversity.
    • Developed an adaptive granularity learning strategy (AGLS) integrating LSH-based clustering and LR-based control to dynamically adjust subpopulation sizes.
    • AGLS aims to balance exploration (escaping suboptima) and exploitation (converging in the search space) based on evolutionary states.

    Main Results:

    • AGLDPSO demonstrated superior or comparable performance against state-of-the-art large-scale optimization algorithms.
    • The algorithm was tested on 35 benchmark functions from IEEE CEC2010 and IEEE CEC2013 large-scale optimization test suites.
    • Experimental results validate the effectiveness of the proposed AGLDPSO in addressing the identified challenges.

    Conclusions:

    • AGLDPSO effectively overcomes the limitations of slow convergence and local optima in large-scale optimization.
    • The integration of LSH and LR within a distributed particle swarm optimization framework provides a robust approach to adaptive granularity control.
    • The proposed algorithm offers a significant advancement for evolutionary computation in tackling complex, large-scale optimization problems.