Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by the behavior of fireflies in nature. Though efficient, its parameters do not change during iterations, which is also true for particle swarm optimization. This paper propose a hybrid model to improve the FA algorithm by introducing learning automata to adjust firefly behavior, and using genetic algorithm to...

Meta-heuristic algorithms prove to be competent in outperforming deterministic algorithms for real-world optimization problems. Firefly algorithm is one such recently developed algorithm inspired by the flashing behavior of fireflies. In this work, a detailed formulation and explanation of the Firefly algorithm implementation is given. Later Firefly algorithm is verified using six unimodal engi...

Many key advances in our understanding of firefly biology and signaling have been made over the past two decades. Here we review this recent research, which includes new phylogenetic results that shed light on the evolution of courtship signal diversity within the family Lampyridae, new insights into firefly flash control, and the discovery of firefly nuptial gifts. We present a comprehensive o...

In this paper, a multimodal firefly algorithm named the CFA (Coulomb Firefly Algorithm) has been presented based on the Coulomb’s law. The algorithm is able to find more than one optimum solution in the problem search space without requiring any additional parameter. In this proposed method, less bright fireflies would be attracted to fireflies which are not only brighter, but according to the ...

We characterized three Japanese firefly species (Luciola lateralis, Luciola cruciata, and Lucidina biplagiata) and three North American firefly species (Lucidota atra, Photuris lucicrescens, and Photuris cinctipennis) based on their surface hydrocarbons. The analysis of firefly extracts by gas chromatography-mass spectrometry (GC-MS) revealed clear differences in the chromatographic profiles an...

Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracte...

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not conside...

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified o...