An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field
Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima...
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9217516/ |
| _version_ | 1818876687529541632 |
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| author | Zenab Mohamed Elgamal Norizan Binti Mohd Yasin Mohammad Tubishat Mohammed Alswaitti Seyedali Mirjalili |
| author_facet | Zenab Mohamed Elgamal Norizan Binti Mohd Yasin Mohammad Tubishat Mohammed Alswaitti Seyedali Mirjalili |
| author_sort | Zenab Mohamed Elgamal |
| collection | DOAJ |
| description | Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon's statistical test ($P$ -value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets. |
| first_indexed | 2024-12-19T13:46:21Z |
| format | Article |
| id | doaj.art-7212c74765e14d9485cfb24322052941 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:46:21Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7212c74765e14d9485cfb243220529412022-12-21T20:18:51ZengIEEEIEEE Access2169-35362020-01-01818663818665210.1109/ACCESS.2020.30297289217516An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical FieldZenab Mohamed Elgamal0https://orcid.org/0000-0002-9927-5893Norizan Binti Mohd Yasin1Mohammad Tubishat2https://orcid.org/0000-0003-1464-8345Mohammed Alswaitti3https://orcid.org/0000-0003-0580-6954Seyedali Mirjalili4https://orcid.org/0000-0002-1443-9458Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, MalaysiaFaculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, MalaysiaSchool of Technology and Computing, Asia Pacific University of Technology and Innovation, Kuala Lumpur, MalaysiaSchool of Electrical and Computer Engineering (ICT), Xiamen University Malaysia, Sepang, MalaysiaCentre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, QLD, AustraliaHarris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon's statistical test ($P$ -value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.https://ieeexplore.ieee.org/document/9217516/Harris Hawks optimization~(HHO) algorithmfeature selectionwrapper methodchaos theorysimulated annealing (SA) |
| spellingShingle | Zenab Mohamed Elgamal Norizan Binti Mohd Yasin Mohammad Tubishat Mohammed Alswaitti Seyedali Mirjalili An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field IEEE Access Harris Hawks optimization~(HHO) algorithm feature selection wrapper method chaos theory simulated annealing (SA) |
| title | An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field |
| title_full | An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field |
| title_fullStr | An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field |
| title_full_unstemmed | An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field |
| title_short | An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field |
| title_sort | improved harris hawks optimization algorithm with simulated annealing for feature selection in the medical field |
| topic | Harris Hawks optimization~(HHO) algorithm feature selection wrapper method chaos theory simulated annealing (SA) |
| url | https://ieeexplore.ieee.org/document/9217516/ |
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