Analysis of Finding Optimal Current in Electric Circuit Using Chimpanzee Leader Election Optimization

The performance of a metaheuristic algorithm is a challenge in its implementation to obtain convergence in a case. The Chimpanzee Leader Election Optimization (CLEO) algorithm is a metaheuristic model that is relatively new and only relies on the ability of random values to get the best solution. This paper explores CLEO's ability to obtain the best results in the case of a simple electrical circuit consisting of a power supply, resistor, and diode. This technique of obtaining the objective function of a simple electrical circuit deserves attention. Even though it is not something new, at least the reader will get a view that is sufficient to understand the context of how to obtain a minimum function that is equal to zero. The CLEO algorithm, which only relies on random values from the program, is not effective enough to find the optimum convergent value, so the learning rate value is introduced in this model even though it does not change the working principle of this algorithm. The results are significantly different when searching for current from a simple electrical circuit until the error is around 10
⁻⁹
from the actual value.