Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design Problems
The slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbalanced development and exploration and the tendency to fall into local optima. To ove...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/9/317 |
| _version_ | 1797492056453545984 |
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| author | Yunyang Zhang Shiyu Du Quan Zhang |
| author_facet | Yunyang Zhang Shiyu Du Quan Zhang |
| author_sort | Yunyang Zhang |
| collection | DOAJ |
| description | The slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbalanced development and exploration and the tendency to fall into local optima. To overcome these drawbacks, an improved SMA with a dynamic quantum rotation gate and opposition-based learning (DQOBLSMA) is proposed in this paper. Specifically, for the first time, two mechanisms are used simultaneously to improve the robustness of the original SMA: the dynamic quantum rotation gate and opposition-based learning. The dynamic quantum rotation gate proposes an adaptive parameter control strategy based on the fitness to achieve a balance between exploitation and exploration compared to the original quantum rotation gate. The opposition-based learning strategy enhances population diversity and avoids falling into the local optima. Twenty-three benchmark test functions verify the superiority of the DQOBLSMA. Three typical engineering design problems demonstrate the ability of the DQOBLSMA to solve practical problems. Experimental results show that the proposed algorithm outperforms other comparative algorithms in convergence speed, convergence accuracy, and reliability. |
| first_indexed | 2024-03-10T00:58:06Z |
| format | Article |
| id | doaj.art-d3307e8bfc3d494492f840e155259d26 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T00:58:06Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-d3307e8bfc3d494492f840e155259d262023-11-23T14:40:17ZengMDPI AGAlgorithms1999-48932022-09-0115931710.3390/a15090317Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design ProblemsYunyang Zhang0Shiyu Du1Quan Zhang2College of Information Science and Engineering, Ningbo University, Ningbo 315211, ChinaEngineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Ningbo 315211, ChinaCollege of Information Science and Engineering, Ningbo University, Ningbo 315211, ChinaThe slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbalanced development and exploration and the tendency to fall into local optima. To overcome these drawbacks, an improved SMA with a dynamic quantum rotation gate and opposition-based learning (DQOBLSMA) is proposed in this paper. Specifically, for the first time, two mechanisms are used simultaneously to improve the robustness of the original SMA: the dynamic quantum rotation gate and opposition-based learning. The dynamic quantum rotation gate proposes an adaptive parameter control strategy based on the fitness to achieve a balance between exploitation and exploration compared to the original quantum rotation gate. The opposition-based learning strategy enhances population diversity and avoids falling into the local optima. Twenty-three benchmark test functions verify the superiority of the DQOBLSMA. Three typical engineering design problems demonstrate the ability of the DQOBLSMA to solve practical problems. Experimental results show that the proposed algorithm outperforms other comparative algorithms in convergence speed, convergence accuracy, and reliability.https://www.mdpi.com/1999-4893/15/9/317slime mold algorithmmetaheuristics algorithmengineering design problemdynamic quantum rotation gateopposition-based learning |
| spellingShingle | Yunyang Zhang Shiyu Du Quan Zhang Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design Problems Algorithms slime mold algorithm metaheuristics algorithm engineering design problem dynamic quantum rotation gate opposition-based learning |
| title | Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design Problems |
| title_full | Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design Problems |
| title_fullStr | Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design Problems |
| title_full_unstemmed | Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design Problems |
| title_short | Improved Slime Mold Algorithm with Dynamic Quantum Rotation Gate and Opposition-Based Learning for Global Optimization and Engineering Design Problems |
| title_sort | improved slime mold algorithm with dynamic quantum rotation gate and opposition based learning for global optimization and engineering design problems |
| topic | slime mold algorithm metaheuristics algorithm engineering design problem dynamic quantum rotation gate opposition-based learning |
| url | https://www.mdpi.com/1999-4893/15/9/317 |
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