Gaslike Social Motility: Optimization Algorithm with Application in Image Thresholding Segmentation

This work introduces a novel and practical metaheuristic algorithm, the Gaslike Social Motility (GSM) algorithm, designed for optimization and image thresholding segmentation. Inspired by a deterministic model that replicates social behaviors using gaslike particles, GSM is characterized by its simplicity, minimal parameter requirements, and emergent social dynamics. These dynamics include: (1) attraction between similar particles, (2) formation of stable particle clusters, (3) division of groups upon reaching a critical size, (4) inter-group interactions that influence particle distribution during the search process, and (5) internal state changes in particles driven by local interactions. The model’s versatility, including cross-group monitoring and adaptability to environmental interactions, makes it a powerful tool for exploring diverse scenarios. GSM is rigorously evaluated against established and recent metaheuristic algorithms, including Particle Swarm Optimization (PSO), Differential Evolution (DE), Bat Algorithm (BA), Artificial Bee Colony (ABC), Artificial Hummingbird Algorithm (AHA), AHA with Aquila Optimization (AHA-AO), Colliding Bodies Optimization (CBO), Enhanced CBO (ECBO), and Social Network Search (SNS). Performance is assessed using 22 benchmark functions, demonstrating GSM’s competitiveness. Additionally, GSM’s efficiency in image thresholding segmentation is highlighted, as it achieves high-quality results with fewer iterations and particles compared to other methods.