Bio-inspired algorithms (BIAs) utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. This survey categorizes BIAs into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their core principles, strengths, and limitations. We illustrate the usage of these algorithms in machine learning, engineering design, bioinformatics, and intelligent systems, and highlight recent advances in hybridization, parameter tuning, and adaptive strategies. Finally, we identify open challenges such as scalability, convergence, reliability, and interpretability to suggest directions for future research. This work aims to serve as a foundational resource for both researchers and practitioners interested in understanding the current landscape and future directions of bio-inspired computing.
View on arXiv@article{somvanshi2025_2506.04238,
title={ A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions },
author={ Shriyank Somvanshi and Md Monzurul Islam and Syed Aaqib Javed and Gaurab Chhetri and Kazi Sifatul Islam and Tausif Islam Chowdhury and Sazzad Bin Bashar Polock and Anandi Dutta and Subasish Das },
journal={arXiv preprint arXiv:2506.04238},
year={ 2025 }
}