Title :
A hybrid group search optimizer with metropolis rule
Author :
Fang, Juanyan ; Cui, Zhihua ; Cai, Xingjuan ; Zeng, Jianchao
Author_Institution :
Complex Syst. & Comput. Intell. Lab., Taiyuan Univ. of Sci. & Technol., Taiyuan, China
Abstract :
Group search optimizer (GSO) is a new novel swarm intelligent technique by simulating animal behavioral ecology. However, as a stochastic optimization algorithm, it is still easily trapped into local optima when dealing with multi-modal optimization problems. Therefore, in this paper, a new variant of GSO is designed by hybriding Metropolis rule to further enhancing the capability escaping from local optima. Simulation results show the performance of this new variant is superior to the standard group search optimizer and particle swarm optimization in multi-model problems especially for high-dimensional cases.
Keywords :
particle swarm optimisation; stochastic programming; animal behavioral ecology simulation; group search optimizer; local optima; metropolis rule; multimodal optimization problem; particle swarm optimization; stochastic optimization algorithm; swarm intelligent technique; Biological system modeling;
Conference_Titel :
Modelling, Identification and Control (ICMIC), The 2010 International Conference on
Conference_Location :
Okayama
Print_ISBN :
978-1-4244-8381-5
Electronic_ISBN :
978-0-9555293-3-7
