Open Access

ARTICLE

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

Deng Yang¹, Chong Zhou^1,*, Xuemeng Wei², Zhikun Chen³, Zheng Zhang⁴

1 School of Information Engineering, Hebei GEO University, Shijiazhuang, 050031, China
2 Faculty of Science, University of Alberta, Edmonton, T2N1N4, Canada
3 College of Resources and Environment, Beibu Gulf University, Qinzhou, 535011, China
4 Clinical Laboratory, The Frist Hospital of Hebei Medical University, Shijiazhuang, 050000, China

* Corresponding Author: Chong Zhou. Email:

(This article belongs to the Special Issue: Bio-inspired Optimization in Engineering and Sciences)

Computer Modeling in Engineering & Sciences 2024, 140(2), 1563-1593. https://doi.org/10.32604/cmes.2024.048049

Received 26 November 2023; Accepted 22 February 2024; Issue published 20 May 2024

Abstract

In classification problems, datasets often contain a large amount of features, but not all of them are relevant for accurate classification. In fact, irrelevant features may even hinder classification accuracy. Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate. Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter, but the results obtained depend on the value of the parameter. To eliminate this parameter’s influence, the problem can be reformulated as a multi-objective optimization problem. The Whale Optimization Algorithm (WOA) is widely used in optimization problems because of its simplicity and easy implementation. In this paper, we propose a multi-strategy assisted multi-objective WOA (MSMOWOA) to address feature selection. To enhance the algorithm’s search ability, we integrate multiple strategies such as Levy flight, Grey Wolf Optimizer, and adaptive mutation into it. Additionally, we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity. Results on fourteen University of California Irvine (UCI) datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance. The source code can be accessed from the website: .

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Keywords

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