Bio Inspired Algorithm

Bio-inspired algorithms draw inspiration from natural processes to solve complex computational problems. Current research focuses on applying these algorithms to diverse fields, including quantum computing, medical scheduling (e.g., radiation therapy optimization), and chronic disease prediction, often employing models like genetic algorithms, particle swarm optimization, and variations of evolutionary algorithms. These algorithms demonstrate effectiveness in optimizing resource allocation, improving prediction accuracy, and streamlining complex processes, thereby impacting various scientific disciplines and practical applications. The ongoing development and refinement of these algorithms, including hybrid approaches combining data-driven and physics-based models, aim to enhance efficiency and solution quality across a wide range of applications.