Abstract
Biologically inspired algorithms (BIAs) are computational methods that model natural processes and systems. Although various types of BIAs have been developed over time, their classification has not been extensively studied. The most widely recognized types include evolution-based algorithms that draw inspiration from natural evolution and swarm-based algorithms that mimic the collective behaviors of animal groups. In addition, ecosystem-based algorithms provide another classification perspective. Many modern BIAs go beyond this well-established framework. These algorithms have shown themselves to be highly adaptable, finding application in a variety of domains to solve challenging optimization and decision-making problems. Examples include solving problems such as network routing, resource planning, graph coloring, and the traveling salesman problem. In artificial intelligence, BIAs have improved neural networks by improving clustering, classification, and prediction accuracy. In the medical field, they have made significant contributions to advances in diagnosis and treatment planning. As BIAs continue to evolve, their integration with advanced technologies such as quantum computing paves the way to overcome challenges such as slow integration and computational inefficiency. Reflecting the adaptability and resilience of natural systems, BIAs are increasingly being used in emerging areas such as cloud computing and wireless sensor networks, highlighting their growing relevance for scalable and reliable problem solving. Research significance: The focus of this research is on investigating biologically inspired algorithms (BIAs), which are computational techniques that model natural processes. These algorithms are crucial for solving complex optimization and decision-making problems in diverse domains such as resource planning, networking, artificial intelligence, and medicine. Despite their widespread applications, the classification of BIAs has received little attention. This research highlights the importance of understanding their classification and categorization, which can lead to more effective algorithm selection and optimization. By reviewing and refining the evolutionary-based, swarm-based, and ecosystem-based algorithm categories, this work helps to improve the theoretical framework of BIAs. In addition, the research explores the potential for improving BIA convergence rates by integrating emerging technologies such as quantum computing, which addresses key challenges in real-world applications. In the end, this research could promote the application of bio-inspired algorithms across a range of domains, enhancing computer efficiency, scalability, and flexibility. Methology: Alternatives: Genetic Algorithm (Evolutionary based), Particle Swarm Optimization (Swarm-based), Ant Colony Optimization (Swarm-based), Artificial Bee Colony Algorithm (Swarm-based). Evaluation Parameters: Convergence Speed, Accuracy, Scalability, Resource Efficiency, Computational Complexity, Energy Consumption, Noise Sensitivity. Result: The results show that Genetic Algorithm (Evolutionary based) received the highest ranking, whereas Artificial Bee Colony Algorithm (Swarm-based) received the lowest ranking. Conclusion: According to the PROMETHEE method, Genetic Algorithm (Evolutionary based) ranks highest in terms of its value for Bio-Inspired Algorithms.