Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming ProblemsRizk M Rizk Allah*
Department of Basic Engineering Science, El-Menoufia University, Shebin El-Kom, Egypt
- *Corresponding Author:
- Rizk M Rizk Allah
Department of Basic Engineering Science
El-Menoufia University, Shebin El-Kom, Egypt
E-mail: rizk_masoud @sh-eng.menofia.edu.eg
Received March 21, 2016; Accepted April 29, 2016; Published April 30, 2016
Citation: Allah RMR (2016) Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems. Int J Swarm Intel Evol Comput 5: 134. doi: 10.4172/2090-4908.1000134
Copyright: © 2016 Allah RMR. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
We propose a novel hybrid algorithm named, FOA-FA to solve the nonlinear programming problems (NLPPs). The main feature of the hybrid algorithm is to integrate the strength of fruit fly optimization algorithm (FOA) in handling continuous optimization and the merit of firefly algorithm (FA) in achieving robust exploration. The methodology of the proposed algorithm consists of two phases. The first one employs a variation on original FOA employing a new adaptive radius mechanism (ARM) for exploring the whole scope around the fruit flies locations to overcome the drawbacks of original FOA which has been continues for the nonnegative orthant problems. The second one incorporates FA to update the previous best locations of fruit flies to avoid the premature convergence. The hybrid algorithm speeds up the convergence and improves the algorithm’s performance. The proposed FOA-FA algorithm is tested on several benchmark problems and two engineering applications. The numerical comparisons have demonstrated its effectiveness and efficiency.