A Novel Hybrid Firefly Algorithm for Global Optimization.
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.
College of Automation, Harbin Engineering University, Harbin, China.
Lina
L
Zhang
College of Automation, Harbin Engineering University, Harbin, China.
Liqiang
L
Liu
School of Science and Technology, Middlesex University, London, United Kingdom.
Xin-She
XS
Yang
College of Science, Harbin Engineering University, Harbin, China.
Yuntao
Y
Dai
Sci Rep. 2015 Mar 19;5:9295
25787315