Article Commentary: Artificial Cardiac Conduction System (ACCS) Metaheuristic

The Artificial Cardiac Conduction System (ACCS) is a novel bio-inspired metaheuristic algorithm that takes inspiration from the human cardiac conduction system to optimize problem-solving. This algorithm utilizes the functional behavior of the human heart, where signals are generated and sent to the heart muscle to initiate contractions.

The ACCS algorithm models four nodes found in the myocardium layer: the sinoatrial node, atrioventricular node, bundle of His, and Purkinje fibers. These nodes play a crucial role in generating and controlling the heart rate. The algorithm implements the mechanisms of controlling the heart rate through these nodes, simulating their behavior in the optimization process.

One of the strengths of the ACCS algorithm lies in its ability to determine the balance between exploitation and exploration during the optimization process. To evaluate its performance, the algorithm was benchmarked on 19 well-known mathematical test functions. This analysis allows for assessing the algorithm’s capability to uncover optimal solutions while exploring different areas of the search space.

In the study, the ACCS algorithm was compared against several established metaheuristic algorithms such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Fast Evolutionary Programming (FEP). These algorithms are known for their effectiveness in solving optimization problems.

The results of the comparative study showcase that the ACCS algorithm is capable of producing competitive results compared to the aforementioned well-known metaheuristics and other conventional methods. This demonstrates the potential of the bio-inspired ACCS algorithm as an effective alternative for optimization tasks across various domains.

Overall, the development of the Artificial Cardiac Conduction System (ACCS) algorithm presents a promising contribution to the field of metaheuristics. By mimicking the human cardiac conduction system, this algorithm incorporates biological principles into optimization, potentially improving the search for optimal solutions in a wide range of applications.

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