Firefly Algorithm for Continuous Constrained Optimization Tasks

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

“…Although biology does not have a complete knowledge to determine all the utilities that firefly luminescence can bring to, at least three functions have been identified (Lukasik & Zak, 2009;Yang, 2008): (i) as a communication tool and appeal to potential partners in the reproduction, (ii) as a bait to lure prey for the firefly, (iii) as a warning mechanism for potential predators reminding them that fireflies have a bitter taste.…”

“…Each member of the swarm explores the problem space taking into account results obtained by others, still applying its own randomized moves as well. The influence of other solutions is controlled by the value of attractiveness (Lukasik & Zak, 2009). …”

A firefly algorithm for solving competitive location-design problem: a case study

“…Firefly algorithm, developed by Yang (2008), is a new population-based technique for solving optimization problem, especially for NP-hard problems, and has been motivated by the simulation of the social behavior of fireflies. Lukasik and Zak (2009) use the firefly algorithm for continuous constrained optimization. Their computational experiments show the efficiency of the firefly algorithm.…”

Process Optimisation via Firefly and Ant Colony Optimisation Elements on the Path of Steepest Ascent for a CSTR

“…They can be categorised into three classes: biologically-based inspiration, e.g. genetic algorithm or GA [2], memetics algorithm or MAs [2], shuffled frog leaping algorithm or SFLA [2], firefly algorithm or FFA [3], bees algorithm or BEES [4], harmony search algorithm or HSA [5], neural network or NN [6], ant colony optimisation or ACO [7], evolutionary programming or EP [8], differential evolution or DE [9] and particle swarm optimisation or PSO [10]. Moreover, there are some with the socially-based inspiration, e.g.…”