Hybrid binary whale with harris hawks for feature selection

Ranya Alwajih, Said Jadid Abdulkadir, Hitham Al Hussian, Norshakirah Aziz, Qasem Al-Tashi, Seyedali Mirjalili, Alawi Alqushaibi

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


A tremendous flow of big data has come from the growing use of digital technology and intelligent systems. This has resulted in an increase in not just the dimensional issues that big data encounters, but also the number of challenges that big data faces, including redundancies and useless features. As a result, feature selection is offered as a method for eliminating unwanted characteristics. This study introduces the BWOAHHO memetic technique, which combines the binary hybrid Whale Optimization Algorithm (WOA) with Harris Hawks Optimization (HHO). A transfer function to transfer continuous characteristics to binary to fulfill the feature selection nature condition. The efficiency of the selected attributes is assessed using a wrapper k-Nearest neighbor (KNN) Classifier. About 18 benchmark datasets obtained from UCI repository were utilized to measure the proposed method’s proficiency. The performance of the novel hybrid technique was evaluated by comparing to that of WOA, HHO, Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), and the WOASAT-2. With the new hybrid feature selection method, the WOA algorithm’s efficiency was improved. Classification accuracy, average fitness, average selected attributes, and computational time were all used as performance indicators. In terms of accuracy, the proposed BWOHHO algorithm compared with 5 similar metaheuristic algorithms. The BWOAHHO had a classification accuracy of 92% in the 18 datasets, which was higher than BWOA (90%), BPSO (82%), and BGA (82%). (83%), the fitness measures of the BWOHHO algorithm are 0.08, which is lower than the average fitness of BWOA, BPSO, and BGA., in terms of selected attribute size compare the proposed BWOAHHO algorithm to the results obtained by the other five techniques The average selected feature sizes for BWOAHHO, BWOA, BHHO, BGA, and WOASAT-2 were 18.07, 20.12, 22.3, 22.32, 22.40, and 15.99, respectively, and computing time for the proposed BWOHHO was 7.36 in second which was the lowest computed value. To determine the significance of BWOAHHO, a statistical one-way ANOVA test was used. When compared to existing algorithms, the proposed approach produced better results.

Original languageEnglish
JournalNeural Computing and Applications
Publication statusPublished - 2022
Externally publishedYes


  • Classification
  • Feature (variable) selection
  • Harris hawks optimization
  • Hybrid feature algorithm
  • Machine learning
  • Whale optimization algorithm


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