Optimization of drop ejection frequency in EHD inkjet printing system using an improved Firefly Algorithm

Swarm intelligence approaches have been used to solve various optimization real-world applications in recent years. Firefly Algorithm (FA) is one of the popular stochastic swarm intelligence paradigms developed in the recent past. In order to solve the slow convergence speed of the standard FA, a new improved Firefly Algorithm (iFA) is proposed in this research. Instead of keeping a constant initial brightness coefficient, a new rule has been proposed for updating the brightness of the fireflies based on a selection probability during generations which leads to a better balance between exploration and exploitation. The efficiency of the iFA has been tested by solving benchmark mathematical functions as well as in a real-world engineering problems. In order to comprehensively compare the performance of the iFA, several other metaheuristic algorithms were used for solving the same benchmark functions and the real-world problems. The iFA shows its superiority in almost all the cases for finding better optima in terms of the objective function value. The droplet ejection speed of an electrohydrodynamic inkjet printing system has been significantly improved by the iFA, which hence fully demonstrates its potential to solve real-world problems. Additionally, the iFA proves its efficiency in solving some challenging, classic engineering design problems with unknown search space. The source codes of the iFA are publicly available at https://www.amitball.com/projects/iFA. (C) 2020 Elsevier B.V. All rights reserved.