Abstract

Bio‑inspired computing refers to a class of computational models and algorithms that take inspiration from biological systems to perform adaptive problem solving. Drawing on mechanisms observed in nature—such as evolution, swarm behavior, neural processing, immune response, and plant growth—bio‑inspired methods seek to emulate robustness, self‑organization, and adaptability exhibited by living systems. Core techniques such as genetic algorithms, particle swarm optimization, ant colony optimization, artificial neural networks, and immune‑inspired algorithms have been widely applied to complex optimization, dynamic planning, learning, scheduling, and control problems across science and engineering. Bio‑inspired models distinguish themselves through decentralized processing, emergent collective intelligence, and flexible adaptation to changing environments without requiring explicit problem modeling. This research synthesizes foundational theories, algorithmic designs, and real‑world applications of bio‑inspired computing, with an emphasis on adaptive problem solving. Through systematic literature synthesis and comparative analysis, this work discusses algorithm behavior, hybrid strategies, performance trade‑offs, and practical implementation challenges. Results indicate that bio‑inspired algorithms often achieve near‑optimal solutions with scalable performance in uncertain and multimodal spaces, though they may suffer from parameter sensitivity and convergence issues. The paper concludes with insights on integrating hybrid approaches, adapting to dynamic problem landscapes, and designing explainable bio‑inspired systems for future complex environments.