A Comparative Study of Symbiotic Organism Search and Glow-Worm Swarm Optimization

• Published in: 2023 International Conference on Computational Intelligence, Networks and Security (ICCINS) pp. 1 - 6
• Main Authors: Shelke, Kavita, Shinde, Subhash
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.12.2023
• Subjects: Benchmark testing; Glow-worm Swarm Optimization; Nature-based algorithms; Optimization; Organisms; Particle swarm optimization; Security; Symbiosis; Symbiotic Organism Search
• DOI: 10.1109/ICCINS58907.2023.10450076

Nature-inspired algorithms have gained a lot of popularity in recent years because they're really good at solving tricky problems and can be used in lots of different areas. This study investigates the effectiveness of nature-based algorithms, specifically Symbiotic Organism Search (SOS), and Glow-worm Swarm Optimization (GSO), in solving optimization problems. The study provides an overview of each algorithm and its under-lying principles, including how they mimic natural processes such as social behavior and bioluminescence. The study then presents a comparative analysis of the algorithms' performance on a set of benchmark functions commonly used in optimization problems. Additionally, the study highlights the strengths and weaknesses of each algorithm, such as SOS's balance between exploration and exploitation, and GSO's robustness to noisy environments. The study also highlights potential applications of these algorithms in various fields and identifies directions for future research.