Solving mechanical engineering problems with metaheuristic algorithms and comparing the results

The goal of optimization is to achieve the "best" design according to a set of prior conditions or constraints. These include maximizing factors such as productivity, power, reliability, longevity, efficiency and availability. Metaheuristic algorithms, one of the optimization techniques, have gained increasing importance in engineering design in recent years due to their simplicity and rapidity in finding solutions. This has led to the proliferation of these metaheuristics and an increased tendency to develop new algorithms. In this thesis, Ant Colony Optimization, Artificial Bee Colony, Salp Swarm Algorithm and Sine Cosine Algorithm, which are metaheuristic optimization methods, and Pressure Vessel Design, Robot Gripper Problem, Roller Bearing Design Problem, Gradual Conical Pulley Problem, Tension Compression Spring, which are real-world problems in the field of mechanical engineering, are used. A research has been conducted on Design, Three-Bar Truss Design Problem, Weight Minimization of Speed Reducer and Welded Beam Design and performance analysis of these metaheuristics. In experimental studies, six different scenarios were gradually determined according to various numbers of iterations and population parameters. According to the results obtained, it was decided which method is the most suitable for this type of problems.