Details

Autor(en) / Beteiligte

• Yin, Hujun
• Gao, Yang
• Chen, Songcan
• Wen, Yimin
• Cai, Guoyong
• Gu, Tianlong
• Du, Junping
• Tallón-Ballesteros, Antonio J
• Zhang, Minling

Titel

Chaotic Brain Storm Optimization Algorithm

Ist Teil von

• Intelligent Data Engineering and Automated Learning - IDEAL 2017, 2017, Vol.10585, p.551-559

Ort / Verlag

Switzerland: Springer International Publishing AG

Erscheinungsjahr

2017

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Swarm intelligence algorithms are stochastic optimization algorithms that are very successfully used for hard optimization problems. Brain storm optimization is a recent swarm intelligence algorithm that has been proven successful in many applications but is still not researched enough. Many swarm intelligence algorithm have been recently improved by introduction of chaotic maps that better than random sequences contributed to search quality. In this paper we propose an improvement of the brain storm optimization algorithm by introducing chaotic maps. Two one-dimensional chaotic maps were incorporated into the original brain storm optimization algorithm. The proposed algorithms were tested on 15 standard benchmark functions from CEC 2013 and compared to the original brain storm optimization algorithm and particle swarm optimization. Our proposed chaos based methods obtained better results where for this set of benchmark functions circle maps were superior.