This work proposes a novel bio-inspired metaheuristic called Artificial Cardiac Conduction System (ACCS) inspired by the human cardiac conduction system. The ACCS algorithm imitates the functional behaviour of the human heart that generates and sends signals to the heart muscle, initiating it to contract. Four nodes in the myocardium layer participate in generating and controlling heart rate, such as the sinoatrial, atrioventricular, bundle of His, and Purkinje fibres. The mechanism of controlling the heart rate through these four nodes is implemented. The algorithm is then benchmarked on 19 well-known mathematical test functions as it can determine the exploitation and exploration capability of the algorithm. The results are verified by a comparative study with Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Fast Evolutionary Programming (FEP). The algorithm undergoes a rigorous evaluation using the CEC-C06 2019 Benchmark Test Functions, illuminating its adeptness in both exploitation and exploration. Validation ensues through a meticulous comparative analysis involving the Dragonfly Algorithm (DA), WOA, PSO, Lagrange Elementary Optimization (Leo), and the Ant Nesting Algorithm (ANA). The results show that the ACCS algorithm can provide very competitive results compared to these well-known metaheuristics and other conventional methods.

翻译：本文提出一种新型生物启发式元启发算法——人工心脏传导系统（ACCS），灵感来源于人体心脏传导系统。ACCS算法模仿人类心脏产生并向心肌发送信号以引发收缩的功能行为。心肌层中的四个节点（窦房结、房室结、希氏束和浦肯野纤维）参与心脏搏动的生成与调控，算法实现了通过这四个节点的协同工作控制心率。基于19个经典数学测试函数验证了算法的开发与探索能力，并与鲸鱼优化算法（WOA）、粒子群优化（PSO）、引力搜索算法（GSA）、差分进化（DE）及快速进化规划（FEP）进行对比研究。算法进一步通过CEC-C06 2019基准测试函数进行严格评估，充分展现其在开发与探索方面的性能。通过蜻蜓算法（DA）、WOA、PSO、拉格朗日初等优化（Leo）及蚁巢算法（ANA）的细致对比分析完成验证。结果表明，ACCS算法相较于这些知名元启发算法及其他传统方法展现出极具竞争力的性能。