Research on energy-saving optimization of ATO for urban rail trains based on the improved particle swarm optimization algorithm
MA Xuchi
CHEN Danfeng
Abstract：To address the issue of premature convergence associated with the rapid convergence characteristic of the Particle Swarm Algorithm(PSO),an Improved Particle Swarm Algorithm(IPSO)is proposed.Firstly,to enhance the diversity of the algorithm,a novel dynamic inertia weight method and an adaptive acceleration co-efficient are introduced to dynamically adjust the search range of particles.Secondly,a new speed update strat-egy is presented,which fully utilizes the information from high-quality solutions to further enhance the conver-gence speed of the algorithm.Finally,to verify the effectiveness of the IPSO algorithm,five standard functions were employed for testing,and the results demonstrated its superiority Moreover,the improved algorithm also yielded optimal results in addressing the energy-saving optimization problem of Automatic Train Operation(ATO)for urban rail transit trains.
Keywords：particle swarm optimization algorithmdynamic inertia weightadaptive acceleration coefficientATO
Publication Date：2025-10-30
Online Publishing Date：2025-11-06(First online date of this platform, not the publication date of the document)
Pages：9( 85-93 )