Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm
In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/12/12/2025 |
| _version_ | 1797545487207759872 |
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| author | Narjes Khatoon Naseri Elankovan A. Sundararajan Masri Ayob Amin Jula |
| author_facet | Narjes Khatoon Naseri Elankovan A. Sundararajan Masri Ayob Amin Jula |
| author_sort | Narjes Khatoon Naseri |
| collection | DOAJ |
| description | In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm. |
| first_indexed | 2024-03-10T14:16:07Z |
| format | Article |
| id | doaj.art-fc095ed60ce24bd4a23982c20359ccc6 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T14:16:07Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-fc095ed60ce24bd4a23982c20359ccc62023-11-20T23:49:17ZengMDPI AGSymmetry2073-89942020-12-011212202510.3390/sym12122025Smart Root Search (SRS): A Novel Nature-Inspired Search AlgorithmNarjes Khatoon Naseri0Elankovan A. Sundararajan 1Masri Ayob2Amin Jula3Centre of Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, MalaysiaCentre of Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, MalaysiaData Mining and Optimization Research Group (DMO), Centre for Artificial Intelligent (CAIT), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, MalaysiaData Mining and Optimization Research Group (DMO), Centre for Artificial Intelligent (CAIT), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, MalaysiaIn this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm.https://www.mdpi.com/2073-8994/12/12/2025combinatorial optimization problemheuristics methodnature-inspired algorithmNP-hard problemplant root |
| spellingShingle | Narjes Khatoon Naseri Elankovan A. Sundararajan Masri Ayob Amin Jula Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm Symmetry combinatorial optimization problem heuristics method nature-inspired algorithm NP-hard problem plant root |
| title | Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm |
| title_full | Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm |
| title_fullStr | Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm |
| title_full_unstemmed | Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm |
| title_short | Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm |
| title_sort | smart root search srs a novel nature inspired search algorithm |
| topic | combinatorial optimization problem heuristics method nature-inspired algorithm NP-hard problem plant root |
| url | https://www.mdpi.com/2073-8994/12/12/2025 |
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