A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs
It provides a resource for researchers and practitioners to understand the current state and future directions of bio-inspired algorithms, but it is incremental as it synthesizes existing knowledge without introducing new methods.
This survey reviews and critiques bio-inspired algorithms, categorizing them into eight groups and highlighting their principles, limitations, and applications in fields like machine learning and engineering, while identifying open challenges such as scalability and reliability.
Bio-inspired algorithms utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. However, a plethora of these algorithms require a more rigorous review before making them applicable to the relevant fields. This survey categorizes these algorithms into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their principles, strengths, novelty, and critical limitations. We provide a critique on the novelty issues of many of these algorithms. We illustrate some of the suitable usage of the prominent 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 resource for both researchers and practitioners interested in understanding the current landscape and future directions of reliable and authentic advancement of bio-inspired algorithms.