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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.
Bio-Inspired Algorithms (BIAs) represent a class of metaheuristic methods inspired by biological and natural processes, designed to solve complex, nonlinear, high-dimensional optimization problems. These algorithms emulate strategies from evolution, swarm behavior, foraging, and immune response systems, offering robust and flexible problem-solving mechanisms.
The fundamental strength of BIAs lies in their ability to traverse vast and complex search spaces efficiently through stochastic, population-based, and adaptive approaches. Unlike classical optimization methods that require gradient information and struggle with local optima, BIAs maintain solution diversity through mechanisms such as mutation, self-organization, and decentralized coordination.
Figure 1: Evolutionary motivation and lineage of major bio-inspired algorithms
This comprehensive survey categorizes bio-inspired algorithms into eight distinct groups, each inspired by different natural phenomena and exhibiting unique characteristics:
Bio-inspired algorithms have demonstrated remarkable versatility across diverse application domains:
Recent research has focused on combining multiple bio-inspired approaches to leverage their complementary strengths. Hybrid algorithms often achieve superior performance by balancing exploration and exploitation capabilities.