Intelligent Pacemaker Control via Metaheuristic Optimization of Proportional–Integral–Derivative Parameters

Abstract

Heart disease remains a major global health issue and is among the leading causes of death and disability. Each year, over 1 million people worldwide receive pacemaker implants to regulate heart rhythm, making them essential biomedical devices. The effectiveness of these devices largely depends on the performance of their controllers. This study focuses on developing a smart heart rate (HR) controller for pacemakers using four metaheuristic optimization techniques: particle swarm optimization, grey wolf optimization, drunkard’s walk optimization (DWO), and ant colony optimization. These algorithms are employed to tune the parameters of a proportional–integral–derivative controller, and their performance is assessed using five different error functions. The results indicate that each optimization technique performs best with specific error functions. Notably, the DWO algorithm combined with the integral time absolute error function achieved the most effective and stable outcomes. The study underscores the importance of selecting suitable optimization methods and error metrics to enhance pacemaker performance, reduce harmful overshoots, and ensure faster, more stable HR regulation, ultimately contributing to better patient health outcomes.

Cite this article as: K. Orbay, M. Sağbaş and M. Demir, “Intelligent pacemaker control via metaheuristic optimization of proportional–integral–derivative parameters,” Electrica, 25, 0097, 2026. doi: 10.5152/electrica.2026.25265.