An improved matrix factorization based model for many-objective optimization recommendation

Cui, Zhihua; Zhao, Peng; Hu, Zhaoming; Zhang, Wensheng; Chen, Jinjun

doi:10.1016/j.ins.2021.07.077

Cited by 38 publications

(18 citation statements)

References 30 publications

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“…Xu et al [30] presented a constrained evolutionary algorithm for path planning of single UAVs, which optimizes the flight distance and risk under the constraints of UAV height, angle, and slope. Xu et al [31] modeled the two objectives of threats and fuel cost with cooperative constraints. An improvement of the gray wolf optimization algorithm was then utilized to solve the aforementioned issue.…”

Section: Related Workmentioning

confidence: 99%

Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

Bai

Fan

et al. 2022

Complex Syst. Model. Simul.

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The trajectory planning of multiple unmanned aerial vehicles (UAVs) is the core of efficient UAV mission execution. Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods. However, multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment. Therefore, a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance, trajectory time, trajectory threat, and trajectory coordination distance costs of UAVs. The NSGA-III algorithm, which overcomes the problems of traditional trajectory planning, is used to solve the model. This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm. Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm, thereby addressing different actual needs.

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Section: Related Workmentioning

confidence: 99%

Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

Bai

Fan

et al. 2022

Complex Syst. Model. Simul.

Self Cite

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“…To verify the effectiveness of the proposed method, this paper uses four evaluation metrics [29] for verification, namely MAE (Mean Absolute Error), ACC (Accuracy), CR (Cover Rate), and F1 (F1-measure). The specific calculation method is shown in Table 2 and Equations ( 5)- (8).…”

Section: Evaluating Metricsmentioning

confidence: 99%

Precise Supervision of Enterprise Environmental Protection Behavior Based on Boolean Matrix Factorization under Low Carbon Background

Zhou

Guo

2022

IJERPH

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Supervising the environmental protection behavior of enterprises is a key strategy to achieve “carbon peaking and carbon neutrality”. This research innovatively proposes the concept of precise supervision, aiming to implement differentiated supervision measures for different types of enterprises, and realize the precise supervision method of enterprise environmental protection, which is different from the traditional supervision mode. Firstly, this paper proposes a novel MEBF+ method based on the benchmark algorithm MEBF, and obtains MEBF++ after incorporating the model bias. Secondly, based on the dataset of environmental supervision and certification of listed Chinese companies, the accuracy and robustness of the proposed method are verified by using multiple evaluation indicators. Finally, based on the analysis of the experimental results, two precise supervision concepts, narrow and broad, are proposed under the low-carbon background. The results show that compared with the benchmark method, the accuracy of the proposed method has been improved to a large extent. In addition, the precise supervision proposed in this paper can help reduce the consumption of manpower and resources as well as unite the public to monitor the environmental protection behavior of enterprises.

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“…Internet of things and big data technologies have made great contributions to promoting social development, such as security detection [1,2], autonomous vehicles [3], risk assessment [4], resources or flow shop scheduling [5][6][7], and so on [8][9][10][11]. Among them, location information plays an important role in biological and industrial production safety [12].…”

Section: Introductionmentioning

confidence: 99%

A modified error-oriented weight positioning model based on DV-Hop

2022

KSII TIIS

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The distance vector-hop (DV-Hop) is one of the emblematic algorithms that use node connectivity for locating, which often accompanies by a large positioning error. To reduce positioning error, the bio-inspired algorithm and weight optimization model are introduced to address positioning. Most scholars argue that the weight value decreases as the hop counts increases. However, this point of view ignores the intrinsic relationship between the error and weight. To address this issue, this paper constructs the relationship model between error and hop counts based on actual communication characteristics of sensor nodes in wireless sensor network. Additionally, we prove that the error converges to 1/6CR when the hop count increase and tendency to infinity. Finally, this paper presents a modified error-oriented weight positioning model, and implements it with genetic algorithm. The experimental results demonstrate excellent robustness and error removal.

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An improved matrix factorization based model for many-objective optimization recommendation

Cited by 38 publications

References 30 publications

Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

Precise Supervision of Enterprise Environmental Protection Behavior Based on Boolean Matrix Factorization under Low Carbon Background

A modified error-oriented weight positioning model based on DV-Hop

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