Quantum-behaved particle swarm optimization using Q-Learning

被引：1
|
作者：
Sheng, Xinyi [1 ]
Sun, Jun [1 ]
Xu, Wenbo [1 ]
机构：
[1] Jiangnan Univ, Wuxi, Peoples R China
来源：
MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY | 2014年 / 556-562卷
关键词：
PSO; QPSO; Q-Learning; parameter control;
D O I：
10.4028/www.scientific.net/AMM.556-562.3965
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Quantum-behaved particle swarm optimization (QPSO) has shown excellent performance in solving optimization problems which inspired by analysis from particle swarm optimization (PSO) and quantum mechanics. In QPSO, the only parameter contraction-expansion coefficient square is vital to the performance of algorithm. This paper employs Q-Learning strategy and presents a novel parameter control method to improve QPSO performance. Then the empirical studies on a suite of well-known benchmark functions are to be performed to test performance. Finally, a further performance comparison between the proposed algorithm and other parameter control methods of QPSO are listed and the simulation results show the efficiency of the proposed QPSO with novel adaptive strategies.
引用
收藏
页码：3965 / 3971
页数：7
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