Fitness-Distance Balance based Symbiotic Organism Search Algorithm for Path Planning of Warehouse Systems

The path planning of Automated Guided Vehicle (AGV) is a core focus of research in intelligent warehousing, with the primary objective of determining the shortest route that circumvents obstacles based on known starting and ending points. Many meta-heuristic search (MHS) algorithms have been applied in path planning, such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and so on. However, MHS algorithms face several limitations in path planning, including the risk of premature convergence to local optima, high demand for computational resources, and poor adaptability in dynamic environments etc. To address these issues, Symbiotic Organism Search (SOS) is applied in intelligent warehousing, and is improved by a Fitness-Distance Balance (FDB) method, named FDB-SOS algorithm. The FDB-SOS algorithm enhances the path planning process by dynamically adjusting search parameters, leading to improved search efficiency. Simulation experiments validate the effectiveness of the proposed method, showing that the FDB-SOS algorithm outperforms the traditional SOS algorithm in both path distance and running time, with an average reduction of 32.83% in path distance and 26.70% in running time.