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Hyperparameter Control Using Fuzzy Logic: Evolving Policies for Adaptive Fuzzy Particle Swarm Optimization Algorithm.

Nicolas Roy^1,2, Charlotte Beauthier³, Alexandre Mayer^1,4

¹Department of Physics, University of Namur, Namur, 5000, Belgium.

Evolutionary Computation

|June 18, 2024

Summary

This summary is machine-generated.

This study introduces a novel fuzzy feedback control method to adapt parameters in particle swarm optimization (PSO), significantly enhancing its performance on complex optimization tasks.

Keywords:

Fuzzy Control Hyperheuristics Particle Swarm Optimization Swarm Intelligence Systematic Algorithm Design

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Area of Science:

Computational Intelligence
Optimization Algorithms
Machine Learning

Background:

Heuristic optimization methods like particle swarm optimization (PSO) require careful parameter tuning for optimal performance.
Adapting algorithm parameters during the optimization process is a key challenge in improving heuristic methods.

Purpose of the Study:

To develop a novel approach for designing parameter adaptation strategies in heuristic optimization using continuous fuzzy feedback control.
To systematically create and evaluate a diverse set of fuzzy-controlled PSO algorithms.

Main Methods:

Implemented a continuous fuzzy feedback control framework to dynamically adjust PSO parameters.
Optimized fuzzy processes beforehand using a training benchmark to maximize performance.
Generated 127 distinct fuzzy PSO algorithms with up to seven fuzzy-controlled parameters.

Main Results:

The newly developed fuzzy PSO algorithms demonstrated superior performance over traditional PSO and existing parameter control variants.
Performance was validated in the Congress on Evolutionary Computation (CEC) 2020 competition for single-objective bound-constrained numerical optimization.
Two specific fuzzy controls showed strong efficacy and dependability in real-world scenarios from CEC 2011.

Conclusions:

Continuous fuzzy feedback control offers an effective mechanism for adaptive parameter tuning in PSO.
The proposed fuzzy PSO algorithms represent a significant advancement in optimization performance, outperforming established methods.
The framework is robust and applicable to both benchmark numerical optimization and real-world problems.