Path optimization for robots using nature-inspired hybrid algorithms: ACO, PSO, and GSO

Path planning represents a fundamental challenge in the domain of artificial intelligence, particularly when autonomous robots are required to operate in complex and dynamic environments. Conventional optimization techniques, including Genetic Algorithms (GA) and Ant Colony Optimization (ACO), have been extensively applied to this problem; however, such single-method approaches frequently encounter limitations in terms of efficiency, adaptability, and robustness. To overcome these challenges, this study introduces a hybrid metaheuristic framework that integrates three nature-inspired algorithms: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Glow-worm Swarm Optimization (GSO). The proposed approach, termed APGO (Ant Colony, Particle Swarm, and Glow-worm Optimizer), capitalizes on the distinct strengths of each algorithm—ACO for shortest-path identification, PSO for rapid convergence, and GSO for effective local search—while simultaneously mitigating their individual weaknesses. The principal contribution of this work lies in demonstrating that the integration of ACO, PSO, and GSO yields a significant improvement in optimizing both path length and travel time. Extensive simulation studies and comparative evaluations against traditional single-algorithm methods confirm that APGO achieves superior performance in terms of efficiency, reliability, and computational effectiveness. These findings provide strong evidence that hybrid metaheuristic strategies can advance the state of the art in autonomous robot navigation and serve as a foundation for future developments in intelligent path planning.