Enhancing particle swarm optimization through artificial neural networks

Abstract

Background The integration of Artificial Neural Networks (ANN) within Particle Swarm Optimization (PSO) aims to enhance optimisation algorithms for robotics and signal processing applications. PSO is an evolutionary computation technique inspired by the social behaviours of birds and fish, where particles iteratively adjust their positions based on individual and collective experiences to optimise solutions. Its simplicity and adaptability make it effective, but performance variability due to parameter sensitivity requires further investigation. This study seeks to improve PSO’s efficiency by integrating ANN, addressing limitations and enhancing swarm intelligence in dynamic robotics applications. Methods The study developed a new form of Particle Swarm Optimization (ANN-PSO) and applied it across two domains: continuous optimization and swarm robotics, using both mathematical benchmarks and the Simbad 3D simulator. For continuous optimization, the ANN-PSO's performance was analyzed through rigorous trials across various complex functions to test its capability to locate global optima. In swarm robotics, controlled experiments involved navigating robotic swarms towards targets while avoiding obstacles, comparing ANN-PSO with standard PSO. The experiments assessed metrics such as navigation efficiency, swarm cohesion, and adaptability to environmental changes, focusing on the integration of neural networks within the PSO framework to enhance operational dynamics. Results The dual application of ANN-PSO demonstrated enhanced performance in both continuous optimization and swarm robotics. In continuous optimization, ANN-PSO showed superior capability in navigating complex landscapes, achieving more precise convergence on global optima and displaying robust exploratory behaviors. In robotic swarm experiments, robots programmed with ANN-PSO exhibited superior navigation strategies, characterized by improved decision-making and optimized pathfinding capabilities compared to those using standard PSO, where robots showed less efficient navigation. These results confirm the efficacy of ANN-PSO in managing diverse and dynamic optimization challenges, effectively enhancing both the depth and scope of search processes. Conclusion This study validates the substantial benefits of integrating artificial neural networks with Particle Swarm Optimization, creating a versatile and powerful tool in ANN-PSO that excels in tackling complex problems across continuous optimization and swarm robotics. The adaptability and enhanced learning capabilities imparted by ANN integration not only improve operational efficiency but also broaden the potential applications of PSO in complex and dynamically changing environments. Future research directions include exploring further applications of ANN-PSO in other complex systems, enhancing algorithmic efficiency, and extending its adaptability to more diverse environmental settings.