A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill Climbing
The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10121429/ |
| _version_ | 1797689595841740800 |
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| author | Anurag Tiwari |
| author_facet | Anurag Tiwari |
| author_sort | Anurag Tiwari |
| collection | DOAJ |
| description | The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent. In earlier studies, the performance of Binary BOA (BBOA) is shown to be superior to various state-of-the-art methods in different optimization issues, such as search space reduction and solving classical engineering problems. Here, BBOA expands the original search space with all possibilities (Exploration) and seeks to determine the best one from all the produced solutions (Exploitation). Generally, the global performance of BBOA depends on the tradeoff between the Exploration and Exploitation phase and hence, produces quality solutions when a suitable tradeoff is maintained. This study introduces an improved and computationally effective variant of conventional BBOA by improving the local search ability of the Butterfly Optimization Algorithm. Initially, twelve binary variants were produced using three different transfer functions (S, U, V-shaped), and solution quality is evaluated in terms of respective fitness function scores. Next, we explored the local search ability of BOA by another recently developed optimization technique, namely, Adaptive <inline-formula> <tex-math notation="LaTeX">$\beta -$ </tex-math></inline-formula>Hill Climbing, to compute quality solutions. This optimization process employed two stochastic operators: <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-operator (Neighborhood operator) and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-operator (Mutation operator) to generate improved offspring compared to parent solutions. This phase is iteratively implemented until the desired level of binary pattern with suitable classification accuracy is obtained. We validated the proposed approach on twenty datasets with eleven state-of-the-art feature selection algorithms. The overall results suggest that the proposed improvements increase the classification accuracy with fewer features on most datasets. In addition, the proposed approach’s time complexity was significantly reduced on eighteenth out of twenty datasets. Moreover, the proposed method effectively balances space exploration and solution exploitation in feature selection problems. |
| first_indexed | 2024-03-12T01:47:51Z |
| format | Article |
| id | doaj.art-e0b27a0ee87c49a0806fcd3821e9683d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T01:47:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e0b27a0ee87c49a0806fcd3821e9683d2023-09-08T23:01:40ZengIEEEIEEE Access2169-35362023-01-0111935119353710.1109/ACCESS.2023.327446910121429A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill ClimbingAnurag Tiwari0https://orcid.org/0000-0001-6816-0113Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaThe Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent. In earlier studies, the performance of Binary BOA (BBOA) is shown to be superior to various state-of-the-art methods in different optimization issues, such as search space reduction and solving classical engineering problems. Here, BBOA expands the original search space with all possibilities (Exploration) and seeks to determine the best one from all the produced solutions (Exploitation). Generally, the global performance of BBOA depends on the tradeoff between the Exploration and Exploitation phase and hence, produces quality solutions when a suitable tradeoff is maintained. This study introduces an improved and computationally effective variant of conventional BBOA by improving the local search ability of the Butterfly Optimization Algorithm. Initially, twelve binary variants were produced using three different transfer functions (S, U, V-shaped), and solution quality is evaluated in terms of respective fitness function scores. Next, we explored the local search ability of BOA by another recently developed optimization technique, namely, Adaptive <inline-formula> <tex-math notation="LaTeX">$\beta -$ </tex-math></inline-formula>Hill Climbing, to compute quality solutions. This optimization process employed two stochastic operators: <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-operator (Neighborhood operator) and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-operator (Mutation operator) to generate improved offspring compared to parent solutions. This phase is iteratively implemented until the desired level of binary pattern with suitable classification accuracy is obtained. We validated the proposed approach on twenty datasets with eleven state-of-the-art feature selection algorithms. The overall results suggest that the proposed improvements increase the classification accuracy with fewer features on most datasets. In addition, the proposed approach’s time complexity was significantly reduced on eighteenth out of twenty datasets. Moreover, the proposed method effectively balances space exploration and solution exploitation in feature selection problems.https://ieeexplore.ieee.org/document/10121429/Butterfly optimization algorithmclassification accuracyconvergence ratefeature selectionlocal optimatransfer function |
| spellingShingle | Anurag Tiwari A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill Climbing IEEE Access Butterfly optimization algorithm classification accuracy convergence rate feature selection local optima transfer function |
| title | A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill Climbing |
| title_full | A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill Climbing |
| title_fullStr | A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill Climbing |
| title_full_unstemmed | A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill Climbing |
| title_short | A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill Climbing |
| title_sort | hybrid feature selection method using an improved binary butterfly optimization algorithm and adaptive x03b2 x2013 hill climbing |
| topic | Butterfly optimization algorithm classification accuracy convergence rate feature selection local optima transfer function |
| url | https://ieeexplore.ieee.org/document/10121429/ |
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