A Gradient-Based Adaptive Quantum-behaved Particle Swarm Optimization
Based on the quantum-behaved particle swarm optimization and gradient-based methods, an improved particle swarm optimization algorithm is proposed. In this modified particle swarm algorithm, particles alternate between utilizing quantum behavior and gradient information to optimize parameters. The a...
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| Vydané v: | 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE) s. 37 - 43 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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01.03.2024
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| Abstract | Based on the quantum-behaved particle swarm optimization and gradient-based methods, an improved particle swarm optimization algorithm is proposed. In this modified particle swarm algorithm, particles alternate between utilizing quantum behavior and gradient information to optimize parameters. The algorithm also incorporates local random search to enhance the search ability. Tests on some benchmark functions across various dimensions demonstrates its strong global search capabilities and precision. The experimental results indicate promising prospects for the application of this algorithm. |
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| AbstractList | Based on the quantum-behaved particle swarm optimization and gradient-based methods, an improved particle swarm optimization algorithm is proposed. In this modified particle swarm algorithm, particles alternate between utilizing quantum behavior and gradient information to optimize parameters. The algorithm also incorporates local random search to enhance the search ability. Tests on some benchmark functions across various dimensions demonstrates its strong global search capabilities and precision. The experimental results indicate promising prospects for the application of this algorithm. |
| Author | Zhang, Jingjing Wang, Qingchun Mei, Hao Wu, Yuchun Guo, Guoping |
| Author_xml | – sequence: 1 givenname: Hao surname: Mei fullname: Mei, Hao email: wa21201022@stu.ahu.edu.cn organization: Anhui University,AHU-IAI AI Joint Laboratory,Hefei,Anhui,China – sequence: 2 givenname: Jingjing surname: Zhang fullname: Zhang, Jingjing email: fannyzjj@ahu.edu.cn organization: School of Electrical Engineering and Automation, Anhui University,Hefei,Anhui,China – sequence: 3 givenname: Qingchun surname: Wang fullname: Wang, Qingchun email: qingchun720@ustc.edu.cn organization: Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei,Anhui,China – sequence: 4 givenname: Yuchun surname: Wu fullname: Wu, Yuchun email: wuyuchun@ustc.edu.cn organization: Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei,Anhui,China – sequence: 5 givenname: Guoping surname: Guo fullname: Guo, Guoping email: gpguo@ustc.edu.cn organization: Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei,Anhui,China |
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| Snippet | Based on the quantum-behaved particle swarm optimization and gradient-based methods, an improved particle swarm optimization algorithm is proposed. In this... |
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| SubjectTerms | Costs gradient Gradient methods Heuristic algorithms optimization particle Performance evaluation Robustness Scalability Sociology swarm |
| Title | A Gradient-Based Adaptive Quantum-behaved Particle Swarm Optimization |
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