An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

Li, Tang; Li, Yaping

doi:10.1155/2022/5343521

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…ACO [42], a solution space search method suitable for fnding optimal routes on graphs MMAS [43], a search method that sets the maximum and minimum pheromone intervals and adopts elite rules PSO [44], a solution search method based on information sharing among groups and evolving from disorder to order PSO update [45], a PSO optimization method for constantly updating particle position and velocity…”

Section: Comparison Algorithm Parametermentioning

confidence: 99%

Energy Dispatching Based on an Improved PSO‐ACO Algorithm

Song

et al. 2023

International Journal of Intelligent Systems

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In order to improve the comprehensive performance of energy dispatching between different sites, the optimization research of particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm is carried out. We proposed a new improved PSO-ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites’ energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO-ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high-quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO-ACO algorithm, Min–Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO-ACO algorithm can plan a site energy dispatching route with shorter route, less time-consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.

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Section: Comparison Algorithm Parametermentioning

confidence: 99%

Energy Dispatching Based on an Improved PSO‐ACO Algorithm

Song

et al. 2023

International Journal of Intelligent Systems

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“…At the early stage of iteration, when the inertia weight is relatively large, the global search ability of the particle is enhanced. In contrast, at the late stage of iteration, when the inertia weight is small, the speed of particles decreases continuously, and the local development ability is enhanced, which is conducive to the refinement of local particle search 24 At the same time, in the process of optimization iteration, the fixed learning factor makes it difficult to meet the nonlinear requirements of the optimization process. Therefore, the nonlinear change strategy of the learning factor is introduced to make the learning factor dynamically adjust in the iterative process of the algorithm.…”

Section: Lstm Neural Network Modelmentioning

confidence: 99%

Research on predicting alrm of Signaling Storm by hybrid LSTM-AM optimized with improved PSO

Tong,

Jia,

Deng

et al. 2023

Preprint

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For the voice services under 5G, the prediction of the IMS signaling storm is the foundation for ensuring the stable operation of Voice over New Radio (VoNR) services and strengthening the intelligent management of 5G networks. Simultaneously, it is also an important approach for operators to enhance their core competitiveness. However, the IP Multimedia Subsystem (IMS) signaling storm prediction alarm function for live network systems is still not robust, with most attention being directed towards equipment fault detection and network element health monitoring. Given this limitation of the live network management system, a method combining 2 modules of prediction and judgment is proposed in this research to realize a more advanced prediction of IMS signaling storms. Among this proposed method, a new neurally network model named Trigonometric Particle Swarm Ptimization-Long Short Term Memory-Attention Mechanism (TRIGPSO-LSTM-AM) is constructed and served as the cornerstone of the method. Firstly, the LSTM-AM model, which combines temporal recurrent neural networks with attention mechanism, is established to predict the key index values of the IMS. At the same time, the traditional Particle Swarm Optimization (PSO) algorithm is improved, and a more intelligent particle swarm optimization algorithm based on trigonometric transformation, named TRIGPSO, is proposed to enhance the convergence of the prediction model. Then, an alarm judgment module is built, and the output of the prediction module is utilized as the input for this module. Clustering is achieved based on the KMeans algorithm, and each class is mapped to the alarm level, thereby informing the network management to execute the corresponding alarm operation. Finally, the effectiveness and rationality of the proposed method are validated through several groups of comparative experiments.

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“…On the one hand, the research on material transportation is the most abundant. The main task is to optimize the transportation of emergency materials by reasonably arranging the number, destination, driving route, and driving time of vehicles under the constraints of emergency environment and conditions (11,12). Also, how to transport emergency supplies to affected locations quickly and safely is an important challenge for decision-makers (13).…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Li,

Wang,

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Considering the uncertainty of urban emergency rescue time, it is difficult for emergency vehicles to maintain a 100% service level continuously. The biggest challenge is to minimize driving distance and costs when the number of vehicles available for deployment is uncertain. In this study, considering the limitation of emergency vehicles, a new integrated emergency vehicle scheduling model for large-scale urban emergency rescue was developed, which is able to tackle multiple emergency tasks at the same time, achieving the shortest path and the lowest cost. Then, the emergency scheduling was simulated in seven urban areas of H City in China to verify the applicability of the proposed model, during which a fast non-dominated sorting multi-objective genetic algorithm with elite strategy was used to solve this model. Moreover, the corresponding algorithm test was conducted to verify the feasibility of the Pareto solution and demonstrate its effectiveness. Moreover, the simulation results show that the used algorithm can reduce computing time by 14.27%, compared with the genetic algorithm. Finally, the optimal scheduling scheme was selected according to the decision-makers’ preferences. This study solved various emergency vehicle scheduling problems under uncertain emergency times, which was beneficial for multilateral cooperation emergency scheduling and provided decision support for emergency command departments for choosing the best emergency scheme.

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An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

Cited by 4 publications

References 31 publications

Energy Dispatching Based on an Improved PSO‐ACO Algorithm

Energy Dispatching Based on an Improved PSO‐ACO Algorithm

Research on predicting alrm of Signaling Storm by hybrid LSTM-AM optimized with improved PSO

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

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