Artificial Cardiac Conduction System: Simulating Heart Function for Advanced Computational Problem Solving
arxiv(2024)
摘要
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.
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