A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection
Interior Search Algorithm (ISA) is a recently proposed metaheuristic inspired by the beautification of objects and mirrors. However, similar to most of the metaheuristic algorithms, ISA also encounters two problems, i.e., entrapment in local optima and slow convergence speed. In the past, chaos theo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-03-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2020.1712788 |
| _version_ | 1797684864687800320 |
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| author | Sankalap Arora Manik Sharma Priyanka Anand |
| author_facet | Sankalap Arora Manik Sharma Priyanka Anand |
| author_sort | Sankalap Arora |
| collection | DOAJ |
| description | Interior Search Algorithm (ISA) is a recently proposed metaheuristic inspired by the beautification of objects and mirrors. However, similar to most of the metaheuristic algorithms, ISA also encounters two problems, i.e., entrapment in local optima and slow convergence speed. In the past, chaos theory has been successfully employed to solve such problems. In this study, 10 chaotic maps are embedded to improve the convergence rate as well as the resulting accuracy of the ISA algorithms. The proposed Chaotic Interior Search Algorithm (CISA) is validated on a diverse subset of 13 benchmark functions having unimodal and multimodal properties. The simulation results demonstrate that the chaotic maps (especially tent map) are able to significantly boost the performance of ISA. Furthermore, CISA is employed as a feature selection technique in which the aim is to remove features which may comprise irrelevant or redundant information in order to minimize the classification error rate. The performance of the proposed approaches is compared with five state-of-the-art algorithms over 21 data sets and the results proved the potential of the proposed binary approaches in searching the optimal feature subsets. |
| first_indexed | 2024-03-12T00:35:56Z |
| format | Article |
| id | doaj.art-2da6772b7e9f4a66956e78a40cb6d238 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:35:56Z |
| publishDate | 2020-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-2da6772b7e9f4a66956e78a40cb6d2382023-09-15T09:33:57ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452020-03-0134429232810.1080/08839514.2020.17127881712788A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature SelectionSankalap Arora0Manik Sharma1Priyanka Anand2DAV UniversityDAV UniversityLovely Professional UniversityInterior Search Algorithm (ISA) is a recently proposed metaheuristic inspired by the beautification of objects and mirrors. However, similar to most of the metaheuristic algorithms, ISA also encounters two problems, i.e., entrapment in local optima and slow convergence speed. In the past, chaos theory has been successfully employed to solve such problems. In this study, 10 chaotic maps are embedded to improve the convergence rate as well as the resulting accuracy of the ISA algorithms. The proposed Chaotic Interior Search Algorithm (CISA) is validated on a diverse subset of 13 benchmark functions having unimodal and multimodal properties. The simulation results demonstrate that the chaotic maps (especially tent map) are able to significantly boost the performance of ISA. Furthermore, CISA is employed as a feature selection technique in which the aim is to remove features which may comprise irrelevant or redundant information in order to minimize the classification error rate. The performance of the proposed approaches is compared with five state-of-the-art algorithms over 21 data sets and the results proved the potential of the proposed binary approaches in searching the optimal feature subsets.http://dx.doi.org/10.1080/08839514.2020.1712788 |
| spellingShingle | Sankalap Arora Manik Sharma Priyanka Anand A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection Applied Artificial Intelligence |
| title | A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection |
| title_full | A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection |
| title_fullStr | A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection |
| title_full_unstemmed | A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection |
| title_short | A Novel Chaotic Interior Search Algorithm for Global Optimization and Feature Selection |
| title_sort | novel chaotic interior search algorithm for global optimization and feature selection |
| url | http://dx.doi.org/10.1080/08839514.2020.1712788 |
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