Cultural algorithm-based quantum-behaved particle swarm optimization

被引:9
作者
Yang, Kaiqiao [1 ]
Maginu, Kenjiro [1 ]
Nomura, Hirosato [1 ]
机构
[1] Kyushu Inst Technol, Dept Artificial Intelligence, Iizuka, Fukuoka, Japan
关键词
quantum-behaved PSO; cultural algorithm; differential evolution; GLOBAL OPTIMIZATION;
D O I
10.1080/00207160802676588
中图分类号
O29 [应用数学];
学科分类号
070104 [应用数学];
摘要
A hybrid quantum-behaved particle swarm optimization (QPSO) based on cultural algorithm (CA), which we call cultural QPSO, is proposed. Although QPSO is a promising algorithm for many optimization problems, it is apt to lose the diversity of the swarm in the later period of the search and prematurely converges to the local optimum. Inspired by the structure of human society, this paper uses the CA model to diversify the QPSO population and improve the QPSO's performance. In this model, the swarm is divided into two sub-swarms: the common particle and the elite particle sub-swarm. If a particle comes from a common sub-swarm, it will evolve according to the QPSO method, and during the evolvement, it will be affected not only by the other common particles but also by the elites. For the elites, the differential evolution (DE) method is adopted for evolvement. After each generation, the elites will be re-elected from the whole swarm according to fitness values. The simulation results on benchmark functions demonstrate that cultural QPSO outperforms the original QPSO for many problems.
引用
收藏
页码:2143 / 2157
页数:15
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