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Open Access

ARTICLE

Artificial Circulation System Algorithm: A Novel Bio-Inspired Algorithm

Nermin Özcan1,2,*, Semih Utku3, Tolga Berber4
1 Department of Biomedical Technologies, Dokuz Eylül University, Izmir, 35390, Turkey
2 Department of Biomedical Engineering, Iskenderun Technical University, Iskenderun, 31200, Turkey
3 Department of Computer Engineering, Dokuz Eylül University, Izmir, 35390, Turkey
4 Department of Statistics and Computer Sciences, Karadeniz Technical University, Trabzon, 61080, Turkey
* Corresponding Author: Nermin Özcan. Email: email
(This article belongs to the Special Issue: Advances in Swarm Intelligence Algorithms)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.055860

Received 09 July 2024; Accepted 11 October 2024; Published online 07 November 2024

Abstract

Metaheuristics are commonly used in various fields, including real-life problem-solving and engineering applications. The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm (ACSA). The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process. The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions, identified as classical benchmark functions. The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities. Furthermore, the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches, including evolutionary, human, physics, and swarm-based. Subsequently, a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing literature. The results show that the ACSA strategy can quickly reach the global optimum, avoid getting trapped in local optima, and effectively maintain a balance between exploration and exploitation. ACSA outperformed 42 algorithms statistically, according to post-hoc tests. It also outperformed 9 algorithms quantitatively. The study concludes that ACSA offers competitive solutions in comparison to popüler methods.

Keywords

Bio-inspired; evolutionary; heuristic; metaheuristic; optimization

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