Solving mechanical engineering design problems using metaheuristic methods and comparing performance results

The process of ensuring that something, such as a system or process, works as effectively and efficiently as possible is called optimization. Optimization is important because it can reduce costs, increase efficiency, and improve the quality of outputs, while also helping to improve the performance of a system, process, or model. Design problems in mechanical engineering arise from different types of objectives and varying levels of difficulty, such as equations or inequalities and often non-linear constraints on geometric, kinematic conditions and material resistance. A special type of algorithm called metaheuristics is used to solve such problems. These algorithms aim to find a good enough solution for optimization problems, especially when information is insufficient or computer capacity is limited. In this thesis study, Firefly Algorithm, Gray Wolf Optimization Algorithm, Pathfinder Algorithm and Particle Swarm Optimization algorithm, which are well known in the literature, were used to solve mechanical engineering design problems. These algorithms solve real-world problems in six different scenarios depending on the number of iterations and the number of population (Four-Stage Gearbox Problem, Gas Transmission Compressor Design, Gear Train Design Problem, Himmelblau Function, Hydrostatic Thrust Bearing Design Problem, Multiple Disc Clutch Brake Design Problem, Optimal Design of Industrial Cooling System and Planetary Gear Chain Design Problem) and according to the results obtained, the performances of the metaheuristic algorithms were compared with each other