Performance improvement of ball balancer by using SMO Algorithm Over Hybrid membership Fuzzy technique,conventional fuzzy and Classical PI

The objective of this scholarly article is to provide a thorough examination of the efficacy of the Spider Monkey Optimisation Algorithm (SMOA) in comparison to alternative techniques. In contemporary times, Social Multi-objective Optimisation Algorithm (SMOA) has surfaced as a propitious methodology for enhancing intricate systems. This study assesses the benefits and drawbacks of SMOA and scrutinizes its recent progressions in diverse applications. Additionally, the authors present a new hybrid methodology that integrates SMOA with other optimization algorithms in order to tackle the difficulties presented by complex nonlinear systems.The study is centered on the utilization of SMOA in the context of mobile autonomous robots and ball balancer systems. The performance of SMOA is analyzed by the authors and is subsequently compared to traditional optimization methods. The findings indicate that SMOA exhibits superior performance in efficiency, accuracy, and convergence rate when compared to Hybrid membership fuzzy, conventional TriMF fuzzy, and PID. The experimental findings indicate that SMOA outperforms traditional algorithms in terms of optimizing the intended systems.