Self-Adaptive Forensic-Based Investigation Algorithm with Dynamic Population for Solving Constraint Optimization Problems
The Forensic-Based Investigation (FBI) algorithm is a novel metaheuristic algorithm. Many researches have shown that FBI is a promising algorithm due to two specific population types. However, there is no sufficient information exchange between these two population types in the original FBI algorith...
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| Published in | International journal of computational intelligence systems Vol. 17; no. 1; pp. 1 - 17 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
24.01.2024
Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1875-6883 1875-6891 1875-6883 |
| DOI | 10.1007/s44196-023-00396-2 |
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| Abstract | The Forensic-Based Investigation (FBI) algorithm is a novel metaheuristic algorithm. Many researches have shown that FBI is a promising algorithm due to two specific population types. However, there is no sufficient information exchange between these two population types in the original FBI algorithm. Therefore, FBI suffers from many problems. This paper incorporates a novel self-adaptive population control strategy into FBI algorithm to adjust parameters based on the fitness transformation from the previous iteration, named SaFBI. In addition to the self-adaptive mechanism, our proposed SaFBI refers to a novel updating operator to further improve the robustness and effectiveness of the algorithm. To prove the availability of the proposed algorithm, we select 51 CEC benchmark functions and two well-known engineering problems to verify the performance of SaFBI. Experimental and statistical results manifest that the proposed SaFBI algorithm performs superiorly compared to some state-of-the-art algorithms. |
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| AbstractList | Abstract The Forensic-Based Investigation (FBI) algorithm is a novel metaheuristic algorithm. Many researches have shown that FBI is a promising algorithm due to two specific population types. However, there is no sufficient information exchange between these two population types in the original FBI algorithm. Therefore, FBI suffers from many problems. This paper incorporates a novel self-adaptive population control strategy into FBI algorithm to adjust parameters based on the fitness transformation from the previous iteration, named SaFBI. In addition to the self-adaptive mechanism, our proposed SaFBI refers to a novel updating operator to further improve the robustness and effectiveness of the algorithm. To prove the availability of the proposed algorithm, we select 51 CEC benchmark functions and two well-known engineering problems to verify the performance of SaFBI. Experimental and statistical results manifest that the proposed SaFBI algorithm performs superiorly compared to some state-of-the-art algorithms. The Forensic-Based Investigation (FBI) algorithm is a novel metaheuristic algorithm. Many researches have shown that FBI is a promising algorithm due to two specific population types. However, there is no sufficient information exchange between these two population types in the original FBI algorithm. Therefore, FBI suffers from many problems. This paper incorporates a novel self-adaptive population control strategy into FBI algorithm to adjust parameters based on the fitness transformation from the previous iteration, named SaFBI. In addition to the self-adaptive mechanism, our proposed SaFBI refers to a novel updating operator to further improve the robustness and effectiveness of the algorithm. To prove the availability of the proposed algorithm, we select 51 CEC benchmark functions and two well-known engineering problems to verify the performance of SaFBI. Experimental and statistical results manifest that the proposed SaFBI algorithm performs superiorly compared to some state-of-the-art algorithms. |
| ArticleNumber | 15 |
| Author | Jin, Ting Gao, Shangce Todo, Yuki Cai, Pengxing Zhang, Yu |
| Author_xml | – sequence: 1 givenname: Pengxing surname: Cai fullname: Cai, Pengxing organization: Faculty of Engineering, University of Toyama – sequence: 2 givenname: Yu surname: Zhang fullname: Zhang, Yu organization: Faculty of Engineering, University of Toyama – sequence: 3 givenname: Ting surname: Jin fullname: Jin, Ting organization: School of Science, Nanjing Forestry University – sequence: 4 givenname: Yuki surname: Todo fullname: Todo, Yuki organization: Faculty of Electrical, Information and Communication Engineering, Kanazawa University – sequence: 5 givenname: Shangce orcidid: 0000-0001-5042-3261 surname: Gao fullname: Gao, Shangce email: gaosc@eng.u-toyama.ac.jp organization: Faculty of Engineering, University of Toyama |
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| Keywords | Metaheuristic algorithm Evolutionary algorithm Engineering problem Optimization problem Self-adaptive mechanism |
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| SubjectTerms | Artificial Intelligence Computational Intelligence Control Engineering Engineering problem Evolutionary algorithm Mathematical Logic and Foundations Mechatronics Metaheuristic algorithm Optimization problem Research Article Robotics Self-adaptive mechanism |
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| Title | Self-Adaptive Forensic-Based Investigation Algorithm with Dynamic Population for Solving Constraint Optimization Problems |
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