A novel evacuation path planning method based on improved genetic algorithm

Zhai, Longzhen; Feng, Shaohong

doi:10.3233/jifs-211214

Cited by 48 publications

(18 citation statements)

References 12 publications

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“…In genetic algorithms, generally speaking, selecting a larger initial population can handle more solutions at the same time, so it is easy to find the global optimal solution. e disadvantage is that it increases the time of each generation selection [27,28], so the population size is generally 20-100. In the optimization process, the crossover probability always controls the crossover operator which plays a dominant role in genetic operations.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Research on Intelligent Retrieval Method of Teaching Resources on Large-Scale Network Platform

Zhang

2022

Mathematical Problems in Engineering

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With the increase in information on various cloud computing platforms, there are more and more teaching documents and videos, which provide sufficient resources for people to learn. Facing the large-scale digital teaching resources, how to quickly and accurately retrieve the required content has become an important research direction in the information field. Especially in the face of heterogeneous, dynamic, and large-scale teaching resources stored in the cloud computing platform, the traditional cloud computing resource retrieval has poor performance and low work efficiency. To solve this problem, a cloud computing platform retrieval method based on genetic algorithm is proposed, which is suitable for intelligent retrieval of teaching resources. Firstly, the teaching resource storage system based on cloud computing platform is analyzed, and the overall architecture of the system and the network topology of cloud storage data are given. Then, a resource retrieval method suitable for cloud computing platform is designed by genetic algorithm, and the convergence performance of genetic algorithm is improved by ant colony algorithm. Finally, the selection algorithm in genetic algorithm is optimized by using random numbers and increasing the number of cycles. The experimental results show that the proposed intelligent retrieval method has greatly improved the Recall and Precision compared with the traditional retrieval methods.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Research on Intelligent Retrieval Method of Teaching Resources on Large-Scale Network Platform

Zhang

2022

Mathematical Problems in Engineering

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“…Qian et al [20] pro-posed a chain restaurant delivery adaptive GA specifically for improved delivery mode, which adjusted the traditional crossover and mutation operators to avoid local optimal. Zhai et al [21] proposed a new evacuation path planning algorithm based on GA and simulated annealing algorithm by improving adaptive genetic operators. Liu et al [22] proposed a new adaptive GA in order to improve the adaptive crossover and mutation operators.…”

Section: Improved Gamentioning

confidence: 99%

An optimized resource scheduling algorithm based on GA and ACO algorithm

Yin

Peng

et al. 2023

Preprint

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With the development of Internet of things (IoT) technology, fog computing has gradually come into people’s vision because of the characteristics of real-time and low delay. Providing computing and storage services at the edge of the network, fog computing can meet the needs of users in delay-sensitive applications as a highly virtualized platform. Resource scheduling plays an important role in fog computing, because it can meet the requirements of customers and improve the quality of service. However, if largescale service requests can not solve the resource scheduling problem effectively, it will reduce resource utilization and user satisfaction. Focus on the above problem, we establish a resource scheduling model named normalization processing, which can achieve the goal of the lowest total cost by finding the optimal pheromone. The optimal resource scheduling results can be obtained by changing the pheromone concentration of ants during the simulated foraging process. At the same time, we propose a new resource scheduling algorithm named new genetic ant colony optimization (NGACO) algorithm, which can update pheromone generation by roulette algorithm. The experimental results show that the resource scheduling performance of NGACO algorithm is 14.7%, 25%, 12.8% lower than that of ACO algorithm in makespan, economic cost, total cost respectively, and load balancing is 34.7% higher than ACO algorithm.

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“…Intelligent optimization algorithms, such as GA and PSO, are popular and have many applications in engineering optimization [39,40]. However, each intelligent optimization algorithm has its strengths and weaknesses.…”

Section: Optimization Algorithm 221 Related Workmentioning

confidence: 99%

Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

Zhou

Ran

2023

Energies

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Shale gas reservoirs have extremely low porosity and permeability, making them challenging to exploit. The best method for increasing recovery in shale gas reservoirs is horizontal well fracturing technology. Hence, fracturing parameter optimization is necessary to enhance shale gas horizontal fracturing well production. Traditional optimization methods, however, cannot meet the requirements for overall optimization of fracturing parameters. As for intelligent optimization algorithms, most have excellent global search capability but incur high computation costs, which limits their usefulness in real-world engineering applications. Thus, a modified genetic algorithm combined based on the Spearman correlation coefficient (SGA) is proposed to achieve the rapid optimization of fracturing parameters. SGA determines the crossover and mutation rates by calculating the Spearman correlation coefficient instead of randomly determining the rates like GA does, so that it could quickly converge to the optimal solution. Within a particular optimization time, SGA could perform better than GA. In this study, a production prediction model is established by the XGBoost algorithm based on the dataset obtained by simulating the shale gas multistage fracturing horizontal well development. The result shows that the XGBoost model performs well in predicting shale gas fracturing horizontal well production. Based on the trained XGBoost model, GA, SGA, and SGD were used to optimize the fracturing parameters with the 30-day cumulative production as the optimization objective. This process has conducted nine fracturing parameter optimization tests under different porosity and permeability conditions. The results show that, compared with GA and SGD, SGA has faster speed and higher accuracy. This study’s findings can help optimize the fracturing parameters faster, resulting in improving the production of shale gas fracturing horizontal wells.

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A novel evacuation path planning method based on improved genetic algorithm

Cited by 48 publications

References 12 publications

Research on Intelligent Retrieval Method of Teaching Resources on Large-Scale Network Platform

Research on Intelligent Retrieval Method of Teaching Resources on Large-Scale Network Platform

An optimized resource scheduling algorithm based on GA and ACO algorithm

Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

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