On the randomized firefly algorithm

Abstract

The firefly algorithm is a stochastic meta-heuristic that incorporates randomness into a search process. Essentially, the randomness is useful when determining the next point in the search space and therefore has a crucial impact when exploring the new solution. In this chapter, an extensive comparison is made between various probability distributions that can be used for randomizing the firefly algorithm, e.g., Uniform, Gaussian, Lévi flights, Chaotic maps, and the Random sampling in turbulent fractal cloud. In line with this, variously randomized firefly algorithms were developed and extensive experiments conducted on a well-known suite of functions. The results of these experiments show that the efficiency of a distributions largely depends on the type of a problem to be solved. © 2014 Springer International Publishing Switzerland.