Exploring Gear Optimization Using Firefly Algorithm

The research paper study about the performance of the Firefly Optimization algorithm incorporating its varying addendum and dedendum gear parameters is examined. The Firefly algorithm, a nature-motivated procedure, is known for its applications in resolving challenging optimization issues. Through our research, we ran a series of experiments by different AP and $B P$ values to explore how they influence the algorithm’s convergence and solution quality. For the sake of our study, we ran ten test cases where the values of AP and BP varied. The algorithm was assessed based on speed of convergence and the quality of final solution. The result shows that the choice of AP and BP parameters of the algorithm influence its behavior. We find that some combinations of these elements speed up the convergence process and yielded better solutions; while other combinations were slower with slightly worse solutions. These results provide room for parameters tuning of Firefly Optimization, where practitioners can now have a chance to change the algorithm to get the most out of various optimization tasks. Knowing about the impact of AP and BP values can aid in the achievement of the best outcomes even for real world sectors like robotics, embedded systems and automation for which optimization is a key aspect in system design and control.