A New Hybrid Fruit Fly Optimization Algorithm For Solving Benchmark Problems

The process of finding the best element (solution) to a given problem is called optimization. Manyalgorithms 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 algorithmwith a potential to solve complex optimization issues. The FOA is developed by Wen Tsao Pan in2011, totally built on the foraging characteristics of Fruit Fly. The algorithm has several varieties ofsearch specially based on vision and olfactory. It has a specific technique to find food quickly, afterdetermine the position, and then fly to the object. FOA is used in many applications, especially in theWireless Sensor Network Coverage Optimization proposed (Ren, Zhichao and Liu, 2018), travellingsalesman problem (Nitin S. Choubey, 2014), Short-term Traffic forecasting (Yuanyuan andYongdong, 2017), and so on. To avoid falling into a local optimum and to overcome the weakness ofthe updating strategies which are used to find optimal solution. We have developed a new hybrid FruitFly Optimization algorithm (HFOA) which uses Sine Cosine Algorithm (SCA) and it powerfulupdating and excellent search capabilities. The developed hybrid is tested on a set of 13 Benchmarktest functions and its performance is compared with other optimization algorithms. The resultsobtained showed the successfulness and efficacy of the new hybrid algorithm HFOA, it outperformsthe other meta-heuristics algorithms.Keywords: Optimization, Fruit Fly optimization algorithm, Sine Cosine Optimization Algorithm,Hybrid Fruit Fly Optimization Algorithm.