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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computational Intelligence

    Background:

    • Evolving Intelligent Systems (EIS) performance relies on antecedent and consequent parameter validity.
    • Current methods prioritize system identification novelty over learned parameter optimality.
    • Autonomous Learning Multiple Model (ALMMo) is a recently developed EIS.

    Purpose of the Study:

    • To introduce a particle swarm-based optimization approach for EIS.
    • To simultaneously optimize antecedent and consequent parameters of ALMMo.
    • To enhance EIS performance through iterative optimal solution searching.

    Main Methods:

    • Implementation of a particle swarm optimization algorithm within the ALMMo framework.
    • Simultaneous optimization of ALMMo's antecedent and consequent parameters.
    • Iterative search for optimal solutions in problem spaces.

    Main Results:

    • The proposed approach effectively enhances ALMMo system performance.
    • Optimization does not negatively impact ALMMo's "one pass" learning capability.
    • ALMMo can recursively incorporate new data patterns post-optimization without full retraining.

    Conclusions:

    • The particle swarm-based optimization approach is validated on real-world benchmark problems.
    • The method successfully optimizes EIS parameters, improving overall system performance.
    • The optimization approach is applicable to other EIS with similar mechanisms.