Multi-plane Disc Clutch Brake Design via New Swarm-Based Metaheuristics

Abstract

An iterative optimization technique inspired by nature is called a metaheuristic algorithm. In literature, these are categorized such as genetic-based, social-based, music-based and swarm-based metaheuristic algorithms. Metaheuristic algorithms involve algorithmic processing based on randomness. These are very suitable for use in the optimization of complex engineering design problems. In fact, in some programming languages, these are accessible over the internet as ready-code methods such as Python via Mealpy. However, the accuracy of the results obtained through these methods is not certain. Furthermore, the number of objectives, number of variables and nonlinearity of constraints of optimization problem etc. directly influence to solution performance of metaheuristics. For this reason, new metaheuristic algorithms continue to emerge frequently. Some well-known optimization problem is utilized to estimate the algorithmic performance of new metaheuristic algorithms. This study presents the optimum design of the well-known multi disc clutch brake design using Mealpy for 4 (four) new swarmbased metaheuristics such as Coati Optimization Algorithm (COA), Fox Optimizer (FOX), Osperey Optimization Algorithm (OOA), and Pelican Optimization Algorithm (POA). Next, the algorithmic performances of the utilized optimization algorithms are statistically evaluated. Thus, it is verified that POA has superior algorithmic performance than COA, FOX and OOA in the minimization of the multiplate disc clutch brake design.