Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attractionmodel and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Communicated by V. Loia. B Hui Wang [email protected] Zhihua Cui [email protected] Hui Sun [email protected] Shahryar Rahnamayan [email protected] Xin-She Yang [email protected] 1 School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China 2 Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330099, China 3 School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China 4 Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada 5 School of Science and Technology, Middlesex University, Hendon Campus, London NW44BT, UK Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants.