Abstract
The Weighted Mean of Vectors Algorithm (INFO) is an enhanced weighted average method that optimizes vector positions using three strategies: updating rule, vector combination, and local search. This algorithm exhibits notable optimization capabilities and high convergence accuracy. However, it is not without limitations; specifically, it tends to become trapped in local optima when addressing multi-peaked functions, suffers from a lack of population diversity, and is prone to premature convergence. To address these issues, this paper presents an improved version of the algorithm, WCINFO, which integrates a weighted voting (WV) strategy and a horizontal and vertical crossover (CC) strategy. The WV strategy facilitates early-stage information exchange among search agents and neighboring individuals, while the CC strategy effectively prevents the algorithm from becoming trapped in local optima. This study evaluates WCINFO’s performance using the IEEE CEC 2017 test set, comparing it against the original INFO algorithm, seven mainstream meta-heuristic algorithms (MAs), and eleven enhanced MAs. The Wilcoxon signed-rank test is employed to assess WCINFO’s performance. The results indicate that WCINFO surpasses the other algorithms in convergence accuracy, speed, and robustness. Furthermore, to ascertain WCINFO’s efficacy in feature selection (FS), its binary variant, BWCINFO, is compared against eight other binary classifiers across 16 publicly available datasets. WCINFO achieved the lowest classification error rates compared to other algorithms and selected the fewest features across all 16 datasets. Additionally, it attained 100% accuracy on six of these datasets, with the size of the feature subsets being less than 35.2% of the original number of features.
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The experimental datasets used for feature selection in this study are primarily sourced from publicly available datasets, such as those from the UCI repository (https://archive.ics.uci.edu/dataset/33/dermatology).
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Acknowledgments
This work was supported in part by the Natural Science Foundation of Zhejiang Province (LZ22F020005), National Natural Science Foundation of China (62076185, 62301367).
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Wang, Z., Chen, Y., Cai, Z. et al. Weighted mean of vectors algorithm with neighborhood information interaction and vertical and horizontal crossover mechanism for feature selection. Appl Intell 55, 85 (2025). https://doi.org/10.1007/s10489-024-05889-x
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DOI: https://doi.org/10.1007/s10489-024-05889-x