A Survey of Nature-Inspired Meta-Heuristic Algorithms in Network Alignment

Network alignment plays a pivotal role in fields such as network science, biology, and social network analysis by identifying common structures and relationships across different networks. The process is challenging due to the diversity of network structures and the necessity to align networks from various domains or periods. To address these challenges, nature-inspired meta-heuristic optimization algorithms have emerged as powerful tools. These algorithms, inspired by natural processes such as evolution, swarm behavior, and other biological phenomena, provide effective solutions for complex optimization problems. This paper offers a thorough examination of the application of these meta-heuristic algorithms to network alignment. It explores a wide range of nature-inspired algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing. The review delves into the underlying principles of each algorithm, their practical applications, and their performance in network alignment tasks. By analyzing detailed discussions and practical examples, the paper highlights the strengths and limitations of various meta-heuristic algorithms. It assesses their effectiveness in aligning networks across different scenarios, providing valuable insights for researchers and practitioners. The findings emphasize the potential of these algorithms in overcoming the complexities of network alignment, offering guidance for employing these techniques effectively. The paper also explores future research directions, suggesting ways to advance the field by leveraging nature-inspired algorithms. As a comprehensive resource, it consolidates existing knowledge and enhances understanding, supporting the development of innovative solutions and improved strategies for network alignment.