A New Hybrid Fruit Fly Optimization Algorithm For Solving Benchmark Problems


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Danacı M., Dıallo M. A.

EUROASIA JOURNAL OF MATHEMATICS-ENGINEERING NATURAL & MEDICAL SCIENCES, vol.2, no.7, pp.23-27, 2019 (Peer-Reviewed Journal)

Abstract

The process of finding the best element (solution) to a given problem is called optimization. Many

algorithms such as GA (John Holland, 1975), PSO (Eberhart & Kennedy, 1995), ABC (Karaboğa,

2005) etc. have been developed to fix optimization issues. The Fruit Fly Optimization Algorithm

(FOA) is a part of these algorithms, it’s a new category of global optimization evolutionary algorithm

with a potential to solve complex optimization issues. The FOA is developed by Wen Tsao Pan in

2011, totally built on the foraging characteristics of Fruit Fly. The algorithm has several varieties of

search specially based on vision and olfactory. It has a specific technique to find food quickly, after

determine the position, and then fly to the object. FOA is used in many applications, especially in the

Wireless Sensor Network Coverage Optimization proposed (Ren, Zhichao and Liu, 2018), travelling

salesman problem (Nitin S. Choubey, 2014), Short-term Traffic forecasting (Yuanyuan and

Yongdong, 2017), and so on. To avoid falling into a local optimum and to overcome the weakness of

the updating strategies which are used to find optimal solution. We have developed a new hybrid Fruit

Fly Optimization algorithm (HFOA) which uses Sine Cosine Algorithm (SCA) and it powerful

updating and excellent search capabilities. The developed hybrid is tested on a set of 13 Benchmark

test functions and its performance is compared with other optimization algorithms. The results

obtained showed the successfulness and efficacy of the new hybrid algorithm HFOA, it outperforms

the other meta-heuristics algorithms.

Keywords: Optimization, Fruit Fly optimization algorithm, Sine Cosine Optimization Algorithm,

Hybrid Fruit Fly Optimization Algorithm.