Heart rate optimizer: a novel bio-inspired metaheuristic algorithm

The increasing complexity of real-world optimization problems demands robust and adaptive metaheuristic approaches. Despite notable progress, many existing algorithms suffer from premature convergence, imbalanced exploration-exploitation trade-offs, and sensitivity to high-dimensional search spaces. Motivated by the adaptive regulation mechanisms of the human cardiovascular system, this paper proposes a novel bio-inspired metaheuristic termed the Heart Rate Optimizer (HRO). The algorithm models heart rate variability and autonomic nervous system dynamics to enhance optimization performance, incorporating tachycardia for accelerated global exploration, bradycardia for intensified local exploitation, and Lévy flight-based arrhythmic behavior to escape local optima. In addition, an Orthogonal Learning strategy is integrated to regulate the interaction between exploration and exploitation while preserving population diversity. Extensive experiments conducted on the IEEE CEC2017 and CEC2022 benchmark suites demonstrate that HRO achieves superior solution accuracy, faster convergence, and improved stability compared to nine state-of-the-art algorithms. Moreover, its effectiveness is validated on challenging engineering design problems, including the welded beam, pressure vessel, and tension-compression spring designs, confirming the robustness and practical relevance of the proposed approach.