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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Veröffentlicht in:2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE) S. 37 - 43
Hauptverfasser: Mei, Hao, Zhang, Jingjing, Wang, Qingchun, Wu, Yuchun, Guo, Guoping
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.03.2024
Schlagworte:
Costs
gradient
Gradient methods
Heuristic algorithms
optimization
particle
Performance evaluation
Robustness
Scalability
Sociology
swarm
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Zusammenfassung: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.
DOI:10.1109/ICAACE61206.2024.10548727