@article{10.11648/j.ajcst.20250802.13,
  author = {Yam Krishna Poudel and Rajiv Kumar},
  title = {A Next-Generation Metaheuristic Inspired by Nomadic Behavior: The Raute Algorithm
},
  journal = {American Journal of Computer Science and Technology},
  volume = {8},
  number = {2},
  pages = {72-84},
  doi = {10.11648/j.ajcst.20250802.13},
  url = {https://doi.org/10.11648/j.ajcst.20250802.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20250802.13},
  abstract = {The Raute community of Nepal is the one of the last remaining nomadic indigenous groups. Which exhibits a unique lifestyle centered on adaptation, mobility, and resource management. Their dynamic movement patterns, decision-making hierarchy, and sustainable utilization of resources provide inspiration for a novel computational approach. Which is called the Raute Metaheuristic Algorithm. This algorithm applies key aspects of the Raute people's survival strategies to address complex optimization problems. The Rautes continuously migrate to locate optimal resources while avoiding depletion and the Raute Algorithm dynamically balances exploration and exploitation. This is inspired by the Raute leadership structure which has its hierarchical decision-making model allows solutions to adapt based on the best-performing agents. Therefore, it preventing early convergence and ensuring an efficient search for global optima in high-dimensional spaces. Also, the algorithm integrates a resource-based movement probability function and which ensuring strategic migration away from low-quality solutions. To validate the effectiveness of the Raute Algorithm it is apply to the Rastrigin function. Which is a well-known multimodal benchmark problem in optimization. Comparative analysis with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) shows that the Raute Algorithm achieves competitive accuracy. It improves the convergence speed, and superior robustness in avoiding local optima. A comprehensive attribute comparison highlights its scalability, adaptability, and computational efficiency which making it particularly well-suited for dynamic and real-time optimization challenges. The Raute Algorithm presents substantial opportunities in real-world domains beyond theoretical applications which including engineering optimization, artificial intelligence, supply chain management and data science. This research not only enhances computational innovation but also highlights the importance of cultural heritage in shaping innovative problem-solving methodologies while integrating indigenous wisdom into computational intelligence. The Raute Algorithm is the findings which is nature-inspired and human-centric approaches: contribute to the next generation of efficient and adaptive metaheuristic techniques.
},
 year = {2025}
}

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TY  - JOUR
T1  - A Next-Generation Metaheuristic Inspired by Nomadic Behavior: The Raute Algorithm

AU  - Yam Krishna Poudel
AU  - Rajiv Kumar
Y1  - 2025/05/22
PY  - 2025
N1  - https://doi.org/10.11648/j.ajcst.20250802.13
DO  - 10.11648/j.ajcst.20250802.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 72
EP  - 84
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20250802.13
AB  - The Raute community of Nepal is the one of the last remaining nomadic indigenous groups. Which exhibits a unique lifestyle centered on adaptation, mobility, and resource management. Their dynamic movement patterns, decision-making hierarchy, and sustainable utilization of resources provide inspiration for a novel computational approach. Which is called the Raute Metaheuristic Algorithm. This algorithm applies key aspects of the Raute people's survival strategies to address complex optimization problems. The Rautes continuously migrate to locate optimal resources while avoiding depletion and the Raute Algorithm dynamically balances exploration and exploitation. This is inspired by the Raute leadership structure which has its hierarchical decision-making model allows solutions to adapt based on the best-performing agents. Therefore, it preventing early convergence and ensuring an efficient search for global optima in high-dimensional spaces. Also, the algorithm integrates a resource-based movement probability function and which ensuring strategic migration away from low-quality solutions. To validate the effectiveness of the Raute Algorithm it is apply to the Rastrigin function. Which is a well-known multimodal benchmark problem in optimization. Comparative analysis with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) shows that the Raute Algorithm achieves competitive accuracy. It improves the convergence speed, and superior robustness in avoiding local optima. A comprehensive attribute comparison highlights its scalability, adaptability, and computational efficiency which making it particularly well-suited for dynamic and real-time optimization challenges. The Raute Algorithm presents substantial opportunities in real-world domains beyond theoretical applications which including engineering optimization, artificial intelligence, supply chain management and data science. This research not only enhances computational innovation but also highlights the importance of cultural heritage in shaping innovative problem-solving methodologies while integrating indigenous wisdom into computational intelligence. The Raute Algorithm is the findings which is nature-inspired and human-centric approaches: contribute to the next generation of efficient and adaptive metaheuristic techniques.

VL  - 8
IS  - 2
ER  - 

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