Quantcast

Firefly algorithm with neighborhood attraction

Research paper by Hui Wang, Wenjun Wang; Xinyu Zhou; Hui Sun; Jia Zhao; Xiang Yu; Zhihua Cui

Indexed on: 02 Jan '17Published on: 26 Dec '16Published in: Information Sciences



Abstract

Publication date: March 2017 Source:Information Sciences, Volumes 382–383 Author(s): Hui Wang, Wenjun Wang, Xinyu Zhou, Hui Sun, Jia Zhao, Xiang Yu, Zhihua Cui Firefly algorithm (FA) is a new optimization technique based on swarm intelligence. It simulates the social behavior of fireflies. The search pattern of FA is determined by the attractions among fireflies, whereby a less bright firefly moves toward a brighter firefly. In FA, each firefly can be attracted by all other brighter fireflies in the population. However, too many attractions may result in oscillations during the search process and high computational time complexity. To overcome these problems, we propose a new FA variant called FA with neighborhood attraction (NaFA). In NaFA, each firefly is attracted by other brighter fireflies selected from a predefined neighborhood rather than those from the entire population. Experiments are conducted using several well-known benchmark functions. The results show that the proposed strategy can efficiently improve the accuracy of solutions and reduce the computational time complexity.