An improved firefly algorithm for numerical optimization

Firefly algorithm (FA) is a recently introduced algorithm based upon the flashing light pattern of fireflies. This algorithm has proved its significance for various optimization problems but has the problem of trapping in local optima. In this article, in order to, improve the performance of FA, a new improved FA (IFA) has been proposed. The proposed algorithm has been applied to standard state-of-the-art algorithms and it has been found experimentally that IFA provide improved performance with respect to the standard FA, differential evolution (DE), bat algorithm (BA) and flower pollination algorithm (FPA).

[1]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[2]  Rohit Salgotra,et al.  Pattern Synthesis of Linear Antenna Arrays Using Enhanced Flower Pollination Algorithm , 2017 .

[3]  Rohit Salgotra,et al.  Synthesis of linear antenna array using flower pollination algorithm , 2016, Neural Computing and Applications.

[4]  Rohit Salgotra,et al.  A novel bat flower pollination algorithm for synthesis of linear antenna arrays , 2016, Neural Computing and Applications.

[5]  Rohit Salgotra,et al.  Application of mutation operators to flower pollination algorithm , 2017, Expert Syst. Appl..

[6]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[7]  Urvinder Singh,et al.  A Novel Binary Spider Monkey Optimization Algorithm for Thinning of Concentric Circular Antenna Arrays , 2016 .

[8]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

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

[10]  Urvinder Singh,et al.  Optimal Synthesis of Linear Antenna Arrays Using Modified Spider Monkey Optimization , 2016, Arabian Journal for Science and Engineering.

[11]  Rohit Salgotra,et al.  A boolean spider monkey optimization based energy efficient clustering approach for WSNs , 2018, Wirel. Networks.

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

[13]  Liu Xi-en Ant colony algorithm for continuous space optimization , 2009 .

[14]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..