# A Review on Influx of Bio-Inspired Algorithms: Critique and Improvement Needs

arXiv: https://arxiv.org/abs/2506.04238 · HTML: https://mlsanity.com/paper/2506.04238/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 151 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 11 of 11)

- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Flower Pollination Algorithm for Global Optimization](https://mlsanity.com/paper/1312.5673/) (2013)
- [Nature-Inspired Optimization Algorithms: Challenges and Open Problems](https://mlsanity.com/paper/2003.03776/) (2020)
- [Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges](https://mlsanity.com/paper/2008.03620/) (2020)
- [Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)](https://mlsanity.com/paper/2002.08136/) (2020)
- [Bio-inspired Intelligence with Applications to Robotics: A Survey](https://mlsanity.com/paper/2206.08544/) (2022)
- [Multiobjective Firefly Algorithm for Continuous Optimization](https://mlsanity.com/paper/1303.6336/) (2013)
- [Reinforcement learning based parameters adaption method for particle swarm optimization](https://mlsanity.com/paper/2206.00835/) (2022)
- [Salp Swarm Optimization: a Critical Review](https://mlsanity.com/paper/2106.01900/) (2021)
- [The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways](https://mlsanity.com/paper/2501.07515/) (2025)

## Abstract (arXiv)

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.