A simple diversity guided firefly algorithm

Purpose – The purpose of this paper is to present a modified firefly algorithm (FA) considering the population diversity to avoid local optimum and improve the algorithm’s precision. Design/methodology/approach – When the population diversity is below the given threshold value, the fireflies’ positions update according to the modified equation which can dynamically adjust the fireflies’ exploring and exploiting ability. Findings – A novel metaheuristic algorithm called FA has emerged. It is inspired by the flashing behavior of fireflies. In basic FA, randomly generated solutions will be considered as fireflies, and brightness is associated with the objective function to be optimized. However, during the optimization process, the fireflies become more and more similar and gather into the neighborhood of the best firefly in the population, which may make the algorithm prematurely converged around the local solution. Research limitations/implications – Due to different dimensions and different ranges, the po...

[1]  Xin-She Yang,et al.  Firefly Algorithm, Lévy Flights and Global Optimization , 2010, SGAI Conf..

[2]  Ming Xu,et al.  A Multipopulation Firefly Algorithm for Correlated Data Routing in Underwater Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[3]  Xin-She Yang,et al.  Multiobjective firefly algorithm for continuous optimization , 2012, Engineering with Computers.

[4]  Xiao-Song Yang,et al.  Existence of limit cycles in general planar piecewise linear systems of saddle–saddle dynamics , 2013 .

[5]  Reza Akbari,et al.  A rank based particle swarm optimization algorithm with dynamic adaptation , 2011, J. Comput. Appl. Math..

[6]  M. W. Mustafa,et al.  Modified Firefly Algorithm in solving economic dispatch problems with practical constraints , 2012, 2012 IEEE International Conference on Power and Energy (PECon).

[7]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[8]  Xin-She Yang,et al.  Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization , 2010, NICSO.

[9]  Mohammad Reza Meybodi,et al.  Some Hybrid models to Improve Firefly Algorithm Performance , 2012 .

[10]  Leandro dos Santos Coelho,et al.  A chaotic firefly algorithm applied to reliability-redundancy optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[11]  Hema Banati,et al.  Promoting products online using firefly algorithm , 2012, 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA).

[12]  M. Meybodi,et al.  A new hybrid algorithm based on Firefly Algorithm and cellular learning automata , 2012, 20th Iranian Conference on Electrical Engineering (ICEE2012).

[13]  Ming-Huwi Horng,et al.  Vector quantization using the firefly algorithm for image compression , 2012, Expert Syst. Appl..

[14]  Shuhao Yu,et al.  Self-Adaptive Step Firefly Algorithm , 2013, J. Appl. Math..

[15]  Mario Ventresca,et al.  A diversity maintaining population-based incremental learning algorithm , 2008, Inf. Sci..

[16]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.