Firefly Algorithm (FA) is one of the recent swarm intelligence methods developed by Xin-She Yang in 2008 [12]. FA is a stochastic, nature-inspired, metaheuristic algorithm that can be applied for solving the hardest optimization problems. The main goal of this paper is to analyze the influence of changing some parameters of the FA when solving bound constrained optimization problems. One of the...

Abstract A significant advancement that occurs during the data cleaning stage is estimating missing data. Studies have shown improper handling leads to inaccurate analysis. Furthermore, most studies indicate occurrence of irrespective correlation between attributes. However, an adaptive search procedure helps determine estimates when correlations attributes are considered in process. Firefly Al...

Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony algorithm-based support vector regression (SAFCA-SVR) system that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Lévy flight, and least squares support vector regression (LS-SVR). First, adaptive approach and rand...

Nature inspired algorithms for their powerfulness, acquire a unique place among the algorithms for optimization. This paper intends to provide a comparison of firefly algorithm (FA) with 3 other nature inspired algorithms; genetic algorithms (GA), particle swarm optimization algorithm (PSO) and ant colony systems (ACS). Traveling salesman problem (TSP) has been used as the problem to be solved ...

For effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation. However, standard firefly algorithm (FA) posted some limitations in its process that can eventually lead to premature convergence, affecting performance adding uncertainty optimization results. To address these constraints, this study introduces an additional novel search mechanis...

Firefly Algorithm (FA) is a nature-inspired optimization algorithm that can be successfully applied to continuous optimization problems. However, lot of practical problems are formulated as discrete optimization problems. In this paper a hybrid discrete firefly algorithm (HDFA) is proposed to solve the multi-objective flexible job shop scheduling problem (FJSP). FJSP is an extension of the clas...

In the paper a modified particle swarm optimization (MPSO) is proposed where concepts from firefly algorithm (FA) are borrowed to enhance the performance of particle swarm optimization (PSO). The modifications focus on the velocity vectors of the PSO, which fully use beneficial information of the position of particles and increase randomization item in the PSO. Finally, the performance of the p...

Automatic facial expression recognition plays an important role in various application domains such as medical imaging, surveillance and human-robot interaction. This research presents a novel facial expression recognition system with modified Local Binary Patterns (LBP) for feature extraction and a modified firefly algorithm (FA) for feature optimization. First, in order to deal with illuminat...

This paper provides a comparative study of the performance of different Transform Domain filters like Wavelet, Contourlet, Bandelet and Curvelet with Firefly Algorithm (FA) applied to despeckle Synthetic Aperture Radar (SAR) images. Initially the feature enhancement and edge detection of speckled SAR image are integrated with improved gain function by shrinking and stretching the Wavelet Co-eff...

Firefly algorithm (FA) is a metaheuristic for global optimization. In this paper, we address the practical testing of a heuristic-based FA (HBFA) for computing optima of discrete nonlinear optimization problems, where the discrete variables are of binary type. An important issue in FA is the formulation of attractiveness of each firefly which in turn affects its movement in the search space. Dy...