Cockroach Swarm Optimization Using A
Neighborhood-Based Strategy
Le Cheng1,2,
Yanhong Song1, and Yuetang Bian3
1College of Computer and Communication Engineering,
Huaian Vocational College of Information Technology, China
2College of Computer and Information,
Hohai University, China
3School of Business, Nanjing Normal University, China
Abstract: The original Cockroach Swarm
Optimization (CSO) algorithm suffers from the problems of slow or premature
convergence. This paper described a new cockroach-inspired algorithm, which is
called CSO with Global and Local neighborhoods (CSOGL). In CSOGL, two kinds of
neighborhood models are designed, in order to increase the diversity of
promising solution. Based on above two neighborhood models, two kinds of novel
chase-swarming behaviors are proposed and applied to CSOGL. Moreover, this
paper also provides a formal convergence proof for the CSOGL algorithm. The
comparison results show that the CSOGL algorithm outperform the existing
cockroach-inspired algorithms.
Keywords: Cockroach
swarm optimization, cockroach-inspired algorithm, CSO with global and local neighborhoods,
premature convergence.
Received January 24, 2016; accepted June 13, 2017