Abstract
Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple particle optimization models is proposed, to avoid the destruction of population diversity caused by single optimization model. Secondly, to avoid the problem of random and blind searching in iterative computation process, the chaotic mapping and the reverse learning mechanism are introduced into the improved whale optimization algorithm. Thirdly, the improved archive mechanism is used to store the non-dominated solutions in the optimization process, and fusion distance is used to maintain the diversity of elite set. Fourthly, a dual-population evolutionary mechanism using archive as an information communication medium is designed to enhance the global convergence improvement of hybrid optimization algorithms. Finally, the optimization results on the benchmark functions show that the ICLHOA can significantly outperform other algorithms for contrast. Furthermore, the ATO Matlab/simulation and hardware-in-the-loop simulation (HILS) results show that the ICLHOA has a better optimization effect than that of the traditional optimization algorithms and improved algorithms.
Highlights
Rail transit has the characteristics of large transport volume, energy saving, environmental protection, comfort, safety, punctuality, reliability, fast and convenient
An improved particle swarm optimization algorithm based on comprehensive learning strategy is proposed, which is denoted as ICLPSO
Based on the genetic algorithm and the elite archiving set, this paper proposes the following improvement strategy to prevent the aggregation of individuals in the population to maintain the diversity of the population, which is denoted as the improved elite archiving set mechanism (IESM)
Summary
Rail transit has the characteristics of large transport volume, energy saving, environmental protection, comfort, safety, punctuality, reliability, fast and convenient. Based on research in ATO on optimizing an energy-efficient speed profiled and designing control algorithms to track the speed profile, two intelligent train operation (ITO) algorithms without using precise train model information and offline optimized speed profiles are proposed [10]. In response to the problem that the existing intelligent optimization algorithms and its improved algorithms are easy to fall into local convergence, a hybrid optimization algorithm based on comprehensive learning strategy is proposed in this paper. In order to verify the algorithm performance of ICLHOA, the improved algorithm ICLHOA proposed in this paper and other intelligent optimization algorithms are applied to benchmark functions and optimize the ideal train speed trajectory of Matlab/simulink, isolating ‘control loop’ containing physical hardware devices (ICL)-HILS, retaining ‘control loop’ containing physical hardware devices (RCL)-HILS. The optimization results can show that the improved algorithm proposed in this paper can find a more ideal optimization solution, which has better global optimization performance
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