Title: A novel firefly algorithm for parameter estimation

Authors: Gan Yu

Addresses: School of Information Engineering, Fuyang Normal University, Fuyang 236037, China

Abstract: Firefly algorithm (FA) is a recently proposed optimisation technique, which has shown good optimisation performance. However, FA suffers from slow convergence and low accuracy of solutions. To improve this case, this paper presents a novel FA (NFA) by combining two strategies. First, a local search operator is constructed for better fireflies in the population. Second, a concept of opposition-based learning is used for improving the accuracy of the global best solution. The experiment consists of two parts: (1) seven classical benchmark functions are used to verify the optimisation ability of NFA; and (2) NFA is used for parameter estimation of frequency modulated (FM) sound synthesis. Simulation results show the NFA approach can achieve promising performance.

Keywords: firefly algorithm; local search; opposition; parameter estimation.

DOI: 10.1504/IJWMC.2019.100060

International Journal of Wireless and Mobile Computing, 2019 Vol.16 No.4, pp.290 - 294

Received: 06 Sep 2018
Accepted: 09 Nov 2018

Published online: 30 May 2019 *

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