A NEW HYBRID FRUIT FLY OPTIMIZATION ALGORITHM FOR SOLVING BENCHMARK PROBLEMS
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Keywords:
Optimization, Fruit Fly optimization algorithm, Sine Cosine Optimization Algorithm, Hybrid Fruit Fly Optimization AlgorithmAbstract
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.
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