A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
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.
