Modeling and optimization of urban rail transit scheduling with adaptive fruit fly optimization algorithm

Li, Jin; Xu, Guangming; Wang, Zhengfeng; Wang, Zhanwu

doi:10.1515/phys-2019-0094

Cited by 3 publications

(2 citation statements)

References 17 publications

(14 reference statements)

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“…Li et al believed that with the rapid development of computer technology, artificial intelligence had made great progress in more than 40 years since it was formally proposed in 1956 and had become a broad interdisciplinary and frontier science [4]. Mo et al believe that search engines evolved from search algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm

Gong

2022

Advances in Multimedia

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In order to transform three-dimensional space path planning into two-dimensional plane path planning problem and greatly reduce the search time, an intelligent heuristic search algorithm based on artificial intelligence is proposed. The heuristic search algorithm is analyzed and introduced, and A is chosen. A two-dimensional spatial environment model of picking robot path planning is investigated, and a spatial model of picking robot path planning is established by raster method. Then, considering the whole day operation time, the whole day operation time is divided into several periods. With the help of heuristic search algorithm, the most reasonable operation time interval of each period is found, so as to provide reliable reference for the decision-making organization of urban rail transit operation on how to arrange the train rationally. The experimental results show that the improved A ∗ algorithm can significantly improve the moving path of the picking robot and make the planned path smoother, which confirms the feasibility and superiority of the improved algorithm. The operation decision of urban rail transit is obtained through experiments. After 114 iterations of the heuristic search algorithm, the optimal value is 6.83353635 e -001, while the average optimal value is 6.83551939 e -001. After 231 iterations of particle swarm optimization algorithm, the optimal value is 6.83650785 e -001. The average optimal value is 6.83745935 e -001. After 789 iterations, the genetic algorithm obtains the optimal value of 6.83921100 e -001, and the average optimal value is 6.84410765 e -001. Through the comparative analysis, it can be seen that compared with the other two optimization algorithms, the heuristic search algorithm is significantly better than the other two optimization algorithms, both in terms of the optimal value and the number of optimization iterations. The results show that the heuristic search algorithm is a fast, accurate, and reliable optimization method to solve the problem of accurate scheduling of urban rail transit departure interval. It is proved that the intelligent heuristic search algorithm of artificial intelligence computer can realize the path planning effectively.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm

Gong

2022

Advances in Multimedia

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“…Therefore, to achieve adaptive optimization of the final building ES, this study first elaborates on the mathematical models of various energy supply and EST equipment in the building ES. As one of the energy supply devices, the power output formula of PCs is shown in Equation (1) [20].…”

Section: Building Energy System Model Construction For Smart Elderly ...mentioning

confidence: 99%

Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient

Liu

Xue

2023

Processes

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In smart elderly care communities, optimizing the design of building energy systems is crucial for improving the quality of life and health of the elderly. This study pioneers an innovative adaptive optimization design methodology for building energy systems by harnessing the cutting-edge capabilities of deep reinforcement learning. This avant-garde method initially involves modeling a myriad of energy equipment embedded within the energy ecosystem of smart elderly care community buildings, thereby extracting their energy computation formulae. In a groundbreaking progression, this study ingeniously employs the actor–critic (AC) algorithm to refine the deep deterministic policy gradient (DDPG) algorithm. The enhanced DDPG algorithm is then adeptly wielded to perform adaptive optimization of the operational states within the energy system of a smart retirement community building, signifying a trailblazing approach in this realm. Simulation experiments indicate that the proposed method has better stability and convergence compared to traditional deep Q-learning algorithms. When the environmental interaction coefficient and learning ratio is 4, the improved DDPG algorithm under the AC framework can converge after 60 iterations. The stable reward value in the convergence state is −996. When the scheduling cycle of the energy system is between 0:00 and 8:00, the photovoltaic output of the system optimized by the DDPG algorithm is 0. The wind power output fluctuates within 50 kW. This study realizes efficient operation, energy saving, and emission reduction in building energy systems in smart elderly care communities and provides new ideas and methods for research in this field. It also provides an important reference for the design and operation of building energy systems in smart elderly care communities.

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Energy Consumption Optimization in Rail Transportation System using Linear Programming

Espiritu

Billones

Bugtai

2022

2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environ

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Modeling and optimization of urban rail transit scheduling with adaptive fruit fly optimization algorithm

Cited by 3 publications

References 17 publications

Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm

Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm

Adaptive Optimization Design of Building Energy System for Smart Elderly Care Community Based on Deep Deterministic Policy Gradient

Energy Consumption Optimization in Rail Transportation System using Linear Programming

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