A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions

2025-05-26

arXiv.org

AI Summary

Bio-inspired optimization algorithms—spanning evolutionary, swarm intelligence, and physics-based paradigms—are widely applied to complex optimization problems, yet lack systematic classification and rigorous comparative assessment. Method: This paper introduces the first eight-dimensional unified taxonomy framework, categorizing over 300 algorithms into eight canonical paradigms. It integrates bibliometric analysis, cross-domain case studies, and paradigm-level comparison to synthesize algorithmic principles, applications (e.g., machine learning, engineering design), and emerging research directions—including hybridization, adaptive parameter control, and interpretability. Contribution/Results: The work identifies three fundamental challenges—scalability, convergence guarantees, and reliability—and constructs a structured knowledge graph. It delivers a comprehensive, authoritative survey for researchers and delineates key breakthrough pathways for the next five years, enabling principled algorithm selection, design, and advancement.

Technology Category

• Search and Optimization: Evolutionary Computation
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Bio-inspired Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Categorizing bio-inspired algorithms into eight distinct groups
• Reviewing principles, strengths, and limitations of these algorithms
• Addressing scalability, convergence, and reliability challenges in optimization

Innovation

• Categorizes bio-inspired algorithms into eight groups
• Reviews principles, strengths, and critical limitations
• Highlights hybridization, parameter tuning, adaptive strategies