Digital filters with infinite impulse response (IIR) are essential for preventing local minima mistakes in industrial automation. The researchers created some techniques to improve the IIR filters' ability to detect errors. Fundamentally, an IIR digital filter is a filter that responds recursively. Since a digital IIR filter's error surface is typically multimodal and nonlinear, thorough optimization strategies are necessary to avoid the local minima. Genetic algorithms are normally population-based Metaheuristics. They have been used to solve numerous optimization issues with success. On the other hand, premature convergence is a feature of these classical genetic algorithms that prevents them from looking for a wide range of solutions inside the issue area. This work introduces Particle Swarm Optimization (PSO) based on Swarm Intelligence (SI). PSO is a population-based search algorithm inspired by the reflection of the natural habits of fish schooling and birds flocking in the sky. Using designed filter, the Electrocardiogram (ECG) signal was de-noised by removing the interference. In this paper, a comparison between the genetic algorithm and particle swarm optimization base algorithm has been made to investigate the performance for the minimum performance metrics i.e. MSE (mean-square-error) and MSD (mean standard deviation), through the Coefficients (numerator and denominator) of high-pass IIR digital filter.