Colonial bacterial memetic algorithm and its application on a darts playing robot

• Published in: Scientific reports Vol. 15; no. 1; pp. 10757 - 29
• Main Authors: Kovács, Szilárd, Budai, Csaba, Botzheim, János
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.03.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/705/1042; Adaptability; Algorithms; Bacteria; Constrained optimization; Continuous optimization; Humanities and Social Sciences; Memetic algorithm; Motor task performance; Multi-objective optimization; multidisciplinary; Optimization; Resource consumption; Robotics; Robotics - methods; Science; Science (multidisciplinary); Self-adaptive optimization; Memetic algorithm; Multi-objective optimization; Continuous optimization; Robotics; Constrained optimization; Self-adaptive optimization
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-94245-1

In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CBMA extends the Bacterial Memetic Algorithm by integrating Cultural Algorithms and co-evolutionary dynamics inspired by bacterial group behavior. This combination of natural and artificial evolutionary elements results in a robust algorithm capable of handling complex challenges in robotics, such as constraints, multiple objectives, large search spaces, and complex models, while delivering fast and accurate solutions. CBMA incorporates features like multi-level clustering, dynamic gene selection, hierarchical population clustering, and adaptive co-evolutionary mechanisms, enabling efficient management of task-specific parameters and optimizing solution quality while minimizing resource consumption. The algorithm’s effectiveness is demonstrated through a real-world robotic application, achieving a 100% success rate in a robot arm’s ball-throwing task usually with significantly fewer iterations and evaluations compared to other methods. CBMA was also evaluated using the CEC-2017 benchmark suite, where it consistently outperformed state-of-the-art optimization algorithms, achieving superior outcomes in 71% of high-dimensional cases and demonstrating up to an 80% reduction in required evaluations. These results highlight CBMA’s efficiency, adaptability, and suitability for specialized tasks. Overall, CBMA exhibits exceptional performance in both real-world and benchmark evaluations, effectively balancing exploration and exploitation, and representing a significant advancement in adaptive evolutionary optimization for robotics.