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EODE-PFA: A Multi-Strategy Enhanced Pathfinder Algorithm for Engineering Optimization and Feature Selection.

Meiyan Li1, Chuxin Cao2, Mingyang Du3

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Summary
This summary is machine-generated.

This study introduces an enhanced Pathfinder Algorithm (EODE-PFA) to improve swarm intelligence optimization. The new algorithm balances exploration and exploitation, showing superior performance in benchmark functions and real-world engineering and feature selection problems.

Keywords:
differential evolution algorithmelite opposition-based learningengineering optimizationfeature selectionmulti-strategy enhanced pathfinder algorithm (EODE-PFA)pathfinder algorithmswarm intelligence optimization algorithm

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence Optimization
  • Metaheuristic Algorithms

Background:

  • The original Pathfinder Algorithm (PFA) suffers from imbalanced optimization capabilities, leading to low population diversity and slow convergence.
  • Existing PFA limitations hinder effective global exploration and local exploitation.

Purpose of the Study:

  • To propose an enhanced Pathfinder Algorithm (EODE-PFA) using multi-strategy enhancements.
  • To address the balance issue between global exploration and local optimization in swarm intelligence algorithms.

Main Methods:

  • Developed the Enhanced Pathfinder Algorithm based on multi-strategy (EODE-PFA).
  • Validated EODE-PFA on CEC2022 benchmark functions, complex engineering problems, and feature selection tasks.
  • Compared EODE-PFA against eight established optimization algorithms and conducted ablation experiments.

Main Results:

  • EODE-PFA demonstrated significant advantages in convergence speed and solution accuracy across diverse scenarios.
  • Ablation studies confirmed the synergistic benefits of the implemented strategies.
  • Statistical analysis using the Wilcoxon signed-rank test validated the significance of the results.

Conclusions:

  • EODE-PFA effectively balances exploration and exploitation, outperforming existing algorithms.
  • The proposed algorithm exhibits strong engineering practicality and universality across various optimization scenarios, including discrete feature selection.