The electro-search algorithm (ESO) encounters challenges arising from its slow convergence rate and propensity to descend into local optima. In this study, a hybrid variant based on simulated annealing (SA), termed electro search simulated annealing (ESSA), is proposed to tackle these issues and surmount the obstacles. SA assists the proposed ESSA in escaping local optima through the cooling process while propelling individuals within the population. As these propelled individuals search for new positions, they engage in exploration and consequently approach the global optimum. This establishes a balance between exploitation and exploration for ESSA. ESSA has been compared with 10 metaheuristic algorithms on 15 benchmark functions with dimensions of 100, 500, and 1,000. The experimental results demonstrate its high-solution accuracy. Moreover, ESSA has been tested in the optimization of a robotic arm, a technology that requires low-error rates in the medical field. The analysis reveals the competitiveness and advantages of the proposed ESSA algorithm.