A Novel Hybrid Firefly Algorithm for Global Optimization

Abstract

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

Hybrid Differential Evolution Based Probabilistic Model to Support Quality Aware Best Path Routing Scheme for Mobile Adhoc Networks

“…This increases the diversity and convergence of the population. The primary difference between the FF and the DE is the manner in which new individuals were generated and used for each of the iterations [22].…”

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

“…The MLP training process continues until the stopping criterion is met. Each splendid firefly can draw the attention of its neighborhood fireflies, regardless of their sex, and the attractiveness is relative to its brightness, which makes the exploration of optimal search space progressively productive [47]. The essential errand in the design of the MLP-FFA model is defining the objective function and formulating the variations of light intensity and attractiveness of fireflies.…”

“…The intensity of light emitted from fireflies diminishes with the distance from its source and due to absorption by the media. Mathematically, the light intensity 'I' varies exponentially with the distance r and light absorption parameter γ, which is represented as follows [36,47,48]:…”

“…where the 1st term in Equation (5) emerges due to the attraction effect and the 2nd term represents the randomization parameter, with α as a scaling co-efficient whose values are registered between 0 and 1, while i is a vector of random variables derived from different distributions, such as the uniform distribution, Gaussian distribution, and Lévy flight. For details related to the mathematics of FFA, one can refer to the following literature: References [36,47,48]. Figure 2 demonstrates a schematic perspective of the process of obtaining the optimal hidden layer weights of MLP with the use of the firefly algorithm for the estimation of daily dew point temperature.…”

Common Benchmark Functions for Metaheuristic Evaluation: A Review

“…Same test functions were also solved by PSO, differential evolution (DE), and BA for comparison with the proposed CSO, as the proposed algorithm outperformed the counterparts. Zhang et al [21] also performed experiments on almost the similar number of functions as in previous work while proposing a hybrid Firefly Algorithm (HFA). This research used 13 functions including 6 unimodal and 7 multimodal functions with 30 dimensions and equal number of experimental runs.…”