Firefly algorithm is a meta-heuristic stochastic search with strong robustness and easy implementation. However, it also has some shortcomings, such as the "oscillation" phenomenon caused by too many attractions, which makes convergence speed slow or premature. In original FA, full attraction model consume lot of evaluation times, time complexity high. Therefore, this paper, novel firefly (EMDm...

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...

Abstract Firefly algorithm (FA) is a new random swarm search optimization that modeled after movement and the mutual attraction of flashing fireflies. The number fitness comparisons attractions in FA varies depending on model. A large can induce oscillations, while small cause early convergence add to computational time complexity. This study aims offer H-GA–FA, hybrid combines two metaheuristi...

The aim of this study is to propose an algorithm for generating minimal test sequences by applying Firefly optimization technique. In this study, we use state machine diagram for the behavioral specification of software. This paper generates the important test sequences for composite states in the state machine diagram under consideration. The generated test sequences are then prioritized based...

A classical issue in many applied \elds is to obtain an approximating surface to a given set of data points. Yis problem arises in Computer-Aided Design and Manufacturing (CAD/CAM), virtual reality, medical imaging, computer graphics, computer animation, and many others. Very o_en, the preferred approximating surface is polynomial, usually described in parametric form. Yis leads to the problem ...

This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females...