MSBWO: A Multi-Strategies Improved Beluga Whale Optimization Algorithm for Feature Selection

Feature selection (FS) is a classic and challenging optimization task in most machine learning and data mining projects. Recently, researchers have attempted to develop more effective methods by using metaheuristic methods in FS. To increase population diversity and further improve the effectiveness...

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Published in:Biomimetics (Basel, Switzerland) Vol. 9; no. 9; p. 572
Main Authors: Fan, Zhaoyong, Xiao, Zhenhua, Li, Xi, Huang, Zhenghua, Zhang, Cong
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.09.2024
MDPI
Subjects:
Accuracy
Algorithms
Analysis
Behavior
beluga whale optimization
binary optimizer
Biomimetics
Data mining
Datasets
Engineering research
Exploitation
Falls (Accidents)
Feature selection
Genetic algorithms
global optimization
Heuristic programming
Machine learning
Mapping
Marine mammals
Mathematical models
Mathematical optimization
Methods
Optimization algorithms
Optimization techniques
Whales & whaling
White whale
China
binary optimizer
feature selection
global optimization
beluga whale optimization
ISSN:2313-7673, 2313-7673
Online Access:Get full text
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Summary:Feature selection (FS) is a classic and challenging optimization task in most machine learning and data mining projects. Recently, researchers have attempted to develop more effective methods by using metaheuristic methods in FS. To increase population diversity and further improve the effectiveness of the beluga whale optimization (BWO) algorithm, in this paper, we propose a multi-strategies improved BWO (MSBWO), which incorporates improved circle mapping and dynamic opposition-based learning (ICMDOBL) population initialization as well as elite pool (EP), step-adaptive Lévy flight and spiral updating position (SLFSUP), and golden sine algorithm (Gold-SA) strategies. Among them, ICMDOBL contributes to increasing the diversity during the search process and reducing the risk of falling into local optima. The EP technique also enhances the algorithm′s ability to escape from local optima. The SLFSUP, which is distinguished from the original BWO, aims to increase the rigor and accuracy of the development of local spaces. Gold-SA is introduced to improve the quality of the solutions. The hybrid performance of MSBWO was evaluated comprehensively on IEEE CEC2005 test functions, including a qualitative analysis and comparisons with other conventional methods as well as state-of-the-art (SOTA) metaheuristic approaches that were introduced in 2024. The results demonstrate that MSBWO is superior to other algorithms in terms of accuracy and maintains a better balance between exploration and exploitation. Moreover, according to the proposed continuous MSBWO, the binary MSBWO variant (BMSBWO) and other binary optimizers obtained by the mapping function were evaluated on ten UCI datasets with a random forest (RF) classifier. Consequently, BMSBWO has proven very competitive in terms of classification precision and feature reduction.
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ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics9090572