Dynamic Robust Particle Swarm Optimization Algorithm Based on Hybrid Strategy

Abstract

Robust optimization over time can effectively solve the problem of frequent solution switching in dynamic environments. In order to improve the search performance of dynamic robust optimization algorithm, a dynamic robust particle swarm optimization algorithm based on hybrid strategy (HS-DRPSO) is proposed in this paper. Based on the particle swarm optimization, the HS-DRPSO combines differential evolution algorithm and brainstorms an optimization algorithm to improve the search ability. Moreover, a dynamic selection strategy is employed to realize the selection of different search methods in the proposed algorithm. Compared with the other two dynamic robust optimization algorithms on five dynamic standard test functions, the results show that the overall performance of the proposed algorithm is better than other comparison algorithms.

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

“…Although the suggested model outperforms other prediction methods in terms of prediction accuracy, the current network structure of the model relies on manual design, so the model's convergence speed needs to be improved, and the proposed SAE-GCN-BiLSTM model lacks generalization performance. In future work, meta heuristic algorithms (Zeng, et al, 2023) will be used to perform spatial automated search optimization on the proposed network, increasing the convergence speed of the network model, thereby enhancing its practicality. In addition, advanced technologies, such as Transformer, will be introduced to deeply improve the structure of the network model, and it will be applied to more datasets from developed cities, thereby improving its generalization performance.…”