International Journal of Swarm Intelligence Research (IJSIR) - Current IssueInternational Journal of Swarm Intelligence Research (IJSIR)https://www.igi-global.com/journal/international-journal-swarm-intelligence-research/1149IGI GlobalenInternational Journal of Swarm Intelligence Research (IJSIR)1947-92631947-9271© 2024 IGI Globalecontent@igi-global.comInternational Journal of Swarm Intelligence Research (IJSIR)https://coverimages.igi-global.com/cover-images/covers/ijsir.pnghttps://www.igi-global.com/journal/international-journal-swarm-intelligence-research/1149A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transithttps://www.igi-global.com/article/a-passenger-flow-prediction-method-using-sae-gcn-bilstm-for-urban-rail-transit/335100To address the problems of existing passenger flow prediction methods such as low accuracy, inadequate learning of spatial features of station topology, and inability to apply to large networks, a SAE-GCN-BiLSTM-based passenger flow forecasting method for urban rail transit is proposed. First, the external features are extracted layer by layer using stacked autoencoder (SAE). Then, graph convolutional network (GCN) is used to capture the spatial features of station topology, and bi-directional long and short-term memory network (BiLSTM) is used to extract the bi-directional temporal features, realizing the extraction of the spatio-temporal features. Finally, external features and spatio-temporal features are fused for accurate prediction of urban rail transit passenger flow. The experimental results show that the proposed method is higher than several other advanced models in the evaluation indexes under different granularities, indicating that the model effectively develops the accuracy and robustness of urban rail transit passenger flow prediction.10.4018/IJSIR.335100International Journal of Swarm Intelligence Research (IJSIR), Volume: 15, Issue: 1 (2024) Pages: 1-21Liu, FanArtificial IntelligenceComputer Science & ITCollective Intelligence2024-01-01T05:00:00Z1511212024-01-01T05:00:00ZA Novel Hybrid Binary Bat Algorithm for Global Optimizationhttps://www.igi-global.com/article//342098In this paper, a novel hybrid binary bat algorithm named HBBA is proposed for global optimization problems. First, to avoid simultaneous updating of bat velocity's dimensional components, i.e., elements of velocity vector, a random black hole model is modified to adapt to binary algorithm for updating in unknown spaces for each dimensional component individually. Through this way, the search ability of bats around current group best is increased greatly. Second, a time-varying v-shaped transfer function, rather than a time-invariant one as in closely-related works, is proposed to map velocity in continuous search space to a binary one. This accelerates the speed to switch individuals' positions, i.e., solutions in binary space. Third, chaotic map is utilized to replace monotonous parameters in original binary bat algorithm, which is beneficial for avoiding premature convergence. Simulation results demonstrate the effectiveness of the proposed algorithm by three types of benchmark functions and unit commitment problem.10.4018/IJSIR.342098International Journal of Swarm Intelligence Research (IJSIR), Volume: 15, Issue: 1 (2024) Pages: 0-0Artificial IntelligenceComputer Science & ITCollective Intelligence2024-01-01T05:00:00Z151002024-01-01T05:00:00ZAn Improved Multi-Objective Brain Storm Optimization Algorithm for Hybrid Microgrid Dispatchhttps://www.igi-global.com/article/an-improved-multi-objective-brain-storm-optimization-algorithm-for-hybrid-microgrid-dispatch/336530The increasing integration of renewable energy sources into microgrids has led to challenges in achieving daily optimal scheduling for hybrid alternating current/direct current microgrids (HMGs). To solve the problem, this article presents a novel hybrid AC/DC microgrid scheduling method based on an improved brain storm optimization (BSO) algorithm. Firstly, with economic and energy storage device health as the primary objective functions, this paper proposes a dispatch model for AC-DC hybrid microgrids. To overcome the limitations of traditional algorithms, including premature convergence and can only find non-inferior solution sets, this article proposes a multi-objective BSO algorithm that integrates learning and selection strategies. Additionally, a fuzzy decision-making method is employed to achieve optimal daily dispatching and select the most suitable compromise solution. Finally, experiments are conducted to verify the effectiveness of the proposed multi-objective optimal scheduling method and to demonstrate the practicality and effectiveness of the method in real application scenarios.10.4018/IJSIR.336530International Journal of Swarm Intelligence Research (IJSIR), Volume: 15, Issue: 1 (2024) Pages: 1-21Zhang, KaiTang, ZiArtificial IntelligenceComputer Science & ITCollective Intelligence2024-01-01T05:00:00Z1511212024-01-01T05:00:00ZA Signal Filtering Method for Magnetic Flux Leakage Detection of Rail Surface Defects Based on Minimum Entropy Deconvolutionhttps://www.igi-global.com/article/a-signal-filtering-method-for-magnetic-flux-leakage-detection-of-rail-surface-defects-based-on-minimum-entropy-deconvolution/332791Magnetic flux leakage (MFL) detection of rail surface defects is an important research field for railway traffic safety. Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. The defect signal is significantly enhanced, and the detection rate of minor defects on the rail surface can be effectively improved.10.4018/IJSIR.332791International Journal of Swarm Intelligence Research (IJSIR), Volume: 15, Issue: 1 (2024) Pages: 1-11Liu, JingSu, ShoubaoGuo, HaifengLu, YuhuaChen, YuexiaArtificial IntelligenceComputer Science & ITCollective Intelligence2024-01-01T05:00:00Z1511112024-01-01T05:00:00Z