A Quantum Behaved Particle Swarm Optimization with a Chaotic Operator

被引：0
|
作者：
Li, Mingming [1 ]
Cao, Dandan [1 ,2 ]
Gao, Hao [3 ]
机构：
[1] Beijing Inst Control Engn, Beijing, Peoples R China
[2] China Acad Space Technol, Hangzhou Inst, Hangzhou, Peoples R China
[3] Nanjing Univ Posts & Commun, Nanjing, Peoples R China
来源：
ARTIFICIAL INTELLIGENCE AND ROBOTICS, ISAIR 2023 | 2024年 / 1998卷
关键词：
quantum behaved; particle swarm optimization; chaotic operator; ARTIFICIAL BEE COLONY; ALGORITHM;
D O I：
10.1007/978-981-99-9109-9_21
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
As a popular population based Evolutionary Algorithm, quantum behaved particle swarm optimization (QPSO) has applied widely in many real-world problems. In this paper, for further enhancing the performance of QPSO, we proposed a popular chaotic map into it. The new chaotic operator not only accelerate the convergence rate but also strengthen the search ability in the total space of the original QPSO. Furthermore, we verify the revised algorithm on some traditional benchmark functions. The final compared results on the images prove the superior of our algorithm.
引用
收藏
页码：212 / 218
页数：7
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