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

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Title: EODE-PFA: A Multi-Strategy Enhanced Pathfinder Algorithm for Engineering Optimization and Feature Selection.
Authors: Li M; School of Science, Hainan University, Haikou 570100, China., Cao C; School of Information and Communication Engineering, Hainan University, Haikou 570100, China., Du M; School of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Source: Biomimetics (Basel, Switzerland) [Biomimetics (Basel)] 2026 Jan 08; Vol. 11 (1). Date of Electronic Publication: 2026 Jan 08.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101719189 Publication Model: Electronic Cited Medium: Internet ISSN: 2313-7673 (Electronic) Linking ISSN: 23137673 NLM ISO Abbreviation: Biomimetics (Basel) Subsets: PubMed not MEDLINE
Imprint Name(s): Original Publication: Basel, Switzerland : MDPI AG, [2016]-
Abstract: The Pathfinder Algorithm (PFA) is a bionic swarm intelligence optimization algorithm inspired by simulating the cooperative movement of animal groups in nature to search for prey. Based on fitness, the algorithm classifies search individuals into leaders and followers. However, PFA fails to effectively balance the optimization capabilities of leaders and followers, leading to problems such as insufficient population diversity and slow convergence speed in the original algorithm. To address these issues, this paper proposes an enhanced pathfinder algorithm based on multi-strategy (EODE-PFA). Through the synergistic effects of multiple improved strategies, it effectively solves the balance problem between global exploration and local optimization of the algorithm. To verify the performance of EODE-PFA, this paper applies it to CEC2022 benchmark functions, three types of complex engineering optimization problems, and six sets of feature selection problems, respectively, and compares it with eight mature optimization algorithms. Experimental results show that in three different scenarios, EODE-PFA has significant advantages and competitiveness in both convergence speed and solution accuracy, fully verifying its engineering practicality and scenario universality. To highlight the synergistic effects and overall gains of multiple improved strategies, ablation experiments are conducted on key strategies. To further verify the statistical significance of the experimental results, the Wilcoxon signed-rank test is performed in this study. In addition, for feature selection problems, this study selects UCI real datasets with different real-world scenarios and dimensions, and the results show that the algorithm can still effectively balance exploration and exploitation capabilities in discrete scenarios.
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Contributed Indexing: Keywords: differential evolution algorithm; elite opposition-based learning; engineering optimization; feature selection; multi-strategy enhanced pathfinder algorithm (EODE-PFA); pathfinder algorithm; swarm intelligence optimization algorithm
Entry Date(s): Date Created: 20260127 Date Completed: 20260127 Latest Revision: 20260131
Update Code: 20260131
PubMed Central ID: PMC12838780
DOI: 10.3390/biomimetics11010057
PMID: 41589974
Database: MEDLINE
Description
Abstract:The Pathfinder Algorithm (PFA) is a bionic swarm intelligence optimization algorithm inspired by simulating the cooperative movement of animal groups in nature to search for prey. Based on fitness, the algorithm classifies search individuals into leaders and followers. However, PFA fails to effectively balance the optimization capabilities of leaders and followers, leading to problems such as insufficient population diversity and slow convergence speed in the original algorithm. To address these issues, this paper proposes an enhanced pathfinder algorithm based on multi-strategy (EODE-PFA). Through the synergistic effects of multiple improved strategies, it effectively solves the balance problem between global exploration and local optimization of the algorithm. To verify the performance of EODE-PFA, this paper applies it to CEC2022 benchmark functions, three types of complex engineering optimization problems, and six sets of feature selection problems, respectively, and compares it with eight mature optimization algorithms. Experimental results show that in three different scenarios, EODE-PFA has significant advantages and competitiveness in both convergence speed and solution accuracy, fully verifying its engineering practicality and scenario universality. To highlight the synergistic effects and overall gains of multiple improved strategies, ablation experiments are conducted on key strategies. To further verify the statistical significance of the experimental results, the Wilcoxon signed-rank test is performed in this study. In addition, for feature selection problems, this study selects UCI real datasets with different real-world scenarios and dimensions, and the results show that the algorithm can still effectively balance exploration and exploitation capabilities in discrete scenarios.
ISSN:2313-7673
DOI:10.3390/biomimetics11010057