An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection
To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/8/1065 |
| _version_ | 1797432249719717888 |
|---|---|
| author | Lewang Zou Shihua Zhou Xiangjun Li |
| author_facet | Lewang Zou Shihua Zhou Xiangjun Li |
| author_sort | Lewang Zou |
| collection | DOAJ |
| description | To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems. |
| first_indexed | 2024-03-09T09:57:39Z |
| format | Article |
| id | doaj.art-b2745ef1debb4cc68621942724e137cd |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T09:57:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-b2745ef1debb4cc68621942724e137cd2023-12-01T23:40:09ZengMDPI AGEntropy1099-43002022-08-01248106510.3390/e24081065An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature SelectionLewang Zou0Shihua Zhou1Xiangjun Li2Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, ChinaKey Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, ChinaKey Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, ChinaTo overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems.https://www.mdpi.com/1099-4300/24/8/1065Harris Hawks Optimizationglobal optimizationdata imbalancefeature selection |
| spellingShingle | Lewang Zou Shihua Zhou Xiangjun Li An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection Entropy Harris Hawks Optimization global optimization data imbalance feature selection |
| title | An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection |
| title_full | An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection |
| title_fullStr | An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection |
| title_full_unstemmed | An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection |
| title_short | An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection |
| title_sort | efficient improved greedy harris hawks optimizer and its application to feature selection |
| topic | Harris Hawks Optimization global optimization data imbalance feature selection |
| url | https://www.mdpi.com/1099-4300/24/8/1065 |
| work_keys_str_mv | AT lewangzou anefficientimprovedgreedyharrishawksoptimizeranditsapplicationtofeatureselection AT shihuazhou anefficientimprovedgreedyharrishawksoptimizeranditsapplicationtofeatureselection AT xiangjunli anefficientimprovedgreedyharrishawksoptimizeranditsapplicationtofeatureselection AT lewangzou efficientimprovedgreedyharrishawksoptimizeranditsapplicationtofeatureselection AT shihuazhou efficientimprovedgreedyharrishawksoptimizeranditsapplicationtofeatureselection AT xiangjunli efficientimprovedgreedyharrishawksoptimizeranditsapplicationtofeatureselection |