Housefly algorithm: A novel metaheuristic approach for global optimization

Solving optimization problems is an important part of engineering system design. To date, various types of optimization algorithms have been developed to search for optimal solutions of engineering optimization problems. In terms of the random properties of algorithms, optimization methods can be divided into two categories, deterministic methods and modern metaheuristic algorithms. In general, most metaheuristic algorithms are inspired by nature. In this thesis, a new metaheuristic algorithm, inspired by nature, is proposed to solve optimization problems. The method we call the Housefly Algorithm (KSA) was developed by considering the natural behavior of black flies. The proposed algorithm has been tested using synthetic comparison functions and real-world problems. The results were compared with metaheuristic methods well known in the literature. According to the results obtained in the experimental studies, the proposed method gave very successful results both in synthetic comparison functions and in real world problems.