HWMWOA: A Hybrid WMA–WOA Algorithm with Adaptive Cauchy Mutation for Global Optimization and Data Classification

Jiali Zhang (), Haichan Li () and Morteza Karimzadeh Parizi
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Jiali Zhang: College of Engineering, Guangzhou College of Technology and Business, Guangzhou, Guangdong 510800, P. R. China
Haichan Li: Department of Network Technology, Software Engineering Institute of Guangzhou, Guangzhou, Guangdong 510990, P. R. China
Morteza Karimzadeh Parizi: Department of Computer Engineering, Faculty of Shahid Chamran, Kerman Branch, Technical and Vocational University (TVU), Kerman, Iran

International Journal of Information Technology & Decision Making (IJITDM), 2023, vol. 22, issue 04, 1195-1252

Abstract: Combinatorial metaheuristic optimization algorithms have newly become a remarkable domain for handling real-world and engineering design optimization problems. In this paper, the Whale Optimization Algorithm (WOA) and the Woodpecker Mating Algorithm (WMA) are combined as HWMWOA. WOA is an effective algorithm with the advantage of global searching ability, where the control parameters are very less. But WOA is more probable to get trapped in the local optimum points and miss diversity of population, therefore suffering from premature convergence. The fundamental goal of the HWMWOA algorithm is to overcome the drawbacks of WOA. This betterment includes three basic mechanisms. First, a modified position update equation of WMA by efficient exploration ability is embedded into HWMWOA. Second, a new self-regulation Cauchy mutation operator is allocated to the proposed hybrid method. Finally, an arithmetic spiral movement with a novel search guide pattern is used in the suggested HWMWOA algorithm. The efficiency of the suggested algorithm is appraised over 48 test functions, and the optimal outcomes are compared with 15 most popular and newest metaheuristic optimization algorithms. Moreover, the HWMWOA algorithm is applied for simultaneously optimizing the parameters of SVM (Support Vector Machine) and feature weighting to handle the data classification problem on several real-world datasets from the UCI database. The outcomes prove the superiority of the suggested hybrid algorithm compared to both WOA and WMA. In addition, the results represent that the HWMWOA algorithm outperforms other efficient techniques impressively.

Keywords: Whale Optimization Algorithm; Woodpecker Mating Algorithm; Cauchy mutation; Support Vector Machine; data classification (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219622022500675

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