Determination of Optimal Component Values in Low-Pass Filter Design Using Red Fox Optimization Algorithm

In this study, the optimization of sixth-order Butterworth low- pass filter components was performed using the recently introduced Red Fox Optimization (RFO) algorithm in the literature. The RFO is a meta-heuristic optimization method inspired by nature, specifically modeling the hunting strategies of foxes. The primary objective of RFO is to optimize the search process effectively, enabling the attainment of optimal results. In this study, the RFO algorithm was implemented in MATLAB to optimize the resistance and capacitance values required for the low-pass filter, taking into account the specified cutoff frequency and quality factors. The E24 industrial series was employed to realize the optimized filter components. To validate the accuracy of the optimized results, the obtained values were simulated and analyzed using LTSpice. The simulations demonstrated that the RFO algorithm achieved an error rate of 0.0113 in the optimal filter design, and these findings were compared with those reported in the literature.