A Multi-Agent Collaborative Work Planning Strategy Based on AFSA-PSO Algorithm

Zuo, Jiankai; Chen, Jiatong; Tan, Yaxin; Wang, Mingshen; Wen, Lishu

doi:10.1109/icris.2019.00073

Cited by 10 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy neural network (FNN) combines the advantages of neural network system and fuzzy system, and it has great advantages in dealing with non-linearity and ambiguity. Support Vector Machine (SVM) is used to solve the problem of data classification and belongs to a kind of supervised learning algorithm [40], [41]. XGBoost is an open source machine learning project and has effectively implemented the GBDT algorithm.…”

Section: E the Comparison Of Different Algorithmsmentioning

confidence: 99%

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Zuo

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Insulated conductors can improve the stability of power transmission and reduce the construction space compared with traditional bare conductors. Therefore, insulated conductors are used more and more in overhead power transmission. However, a major challenge of using insulated overhead conductors (IOC) is that the ordinary protection devices are not able to detect the phase-to-ground faults and something, such as tree branch, hitting conductor events. This may cause an accident such as power failure or electrical fire and result in serious damage. In this paper, a new approach, which is based on Discrete Wavelet Transform (DWT) and Long Short Term Memory network (LSTM) for detecting of IOC fault according to partial discharge, is presented. Firstly, the original signal is denoised by DWT. Secondly, the denoised signal is decomposed and extracted features on different layers by DWT. Finally, IOC fault is detected by LSTM. This method can improve the detection accuracy of IOC fault which is tested on the ENET public data set and compared with other classification methods. INDEX TERMS Insulated overhead conductors, partial discharge, discrete wavelet transform, long short term memory network, fault detection.

show abstract

Section: E the Comparison Of Different Algorithmsmentioning

confidence: 99%

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Zuo

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the GWO algorithm was proposed in 2014, it has attracted wide attention from many scholars due to its superior performance [9][10]. The artificial fish swarm algorithm and particle swarm optimization AFSA-PSO is used to develop aircraft path planning [11]. L.Ge et al [12] made a hybrid forecast of short-term photovoltaic power generation based on the PCA-GWO-GRNN algorithm.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Electrical Fault Detection and Recognition Based on Gray Wolf Optimization and Support Vector Machine

Zou¹,

Lv²,

Li³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the increase of electrical equipment, the hidden danger of its failure also rises. Aiming at the problem that the classification accuracy of the basic traditional classification algorithm is not high when judging whether the electrical failure occurs, this paper proposes a Gray Wolf Optimization Support Vector Machine model to improve the recognition rate of electrical diagnosis, referred to as GWO-SVM. We first collected the most common linear (incandescent lamps) and non-linear (microwave ovens) household electrical appliances in normal working and arc fault waveform signals in real life, and then carried out frequency domain feature extraction. Finally, compared with no-optimized SVM and BP neural network, we employed the gray wolf optimization algorithm to optimize support vector machine, and the accuracy of GWO-SVM reaches 90%.

show abstract

“…For example, Faigl et al model the multigoal path planning as a generalized traveling salesman problem with neighborhoods and design a feasible solution via heuristic algorithms [11]. Zuo et al combine the artificial fish swarm algorithm and particle swarm optimization algorithm to address multiagent cooperative work and path planning problem [12]. Hu et al propose a multiobjective optimization approach based on the COLREGs and Hi-NDS rules for path planning of autonomous surface vehicles [13].…”

Section: Introductionmentioning

confidence: 99%

Supervised Reinforcement Learning for ULV Path Planning in Complex Warehouse Environment

Guo

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The rapid development of the logistics industry leads to an urgent need for intelligent equipment to improve warehouse transportation efficiency. Recent advances in unmanned logistics vehicles (ULVs) make them particularly important in smart warehouses. However, the complex warehouse environment poses a significant challenge to ULV transportation path planning. Multiple ULVs need to transport cargoes with good coordination ability to overcome the low efficiency of a single ULV. The ULVs also need to interact with the environment to achieve optimal path planning with obstacle avoidance. In this paper, we propose a supervised deep reinforcement learning (SDRL) approach for logistics warehouses that enables autonomous ULVs path planning for cargo transportation in a complex environment. The proposed SDRL approach is featured by (1) designing the supervision module to imitate the behaviors of experts and thus improve the coordination ability of multiple ULVs, (2) optimizing the generator of the imitation learning based on the proximal policy optimization to boost the learning performance, and (3) developing the policy module via deep reinforcement learning to avoid obstacles when navigating the ULVs in warehouse environments. The experiments over dynamic and fixed-point warehouse environments show that the proposed SDRL approach outperforms its rivals regarding average reward, training speed, task completion rate, and collision times.

show abstract

A Multi-Agent Collaborative Work Planning Strategy Based on AFSA-PSO Algorithm

Cited by 10 publications

References 2 publications

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Intelligent Electrical Fault Detection and Recognition Based on Gray Wolf Optimization and Support Vector Machine

Supervised Reinforcement Learning for ULV Path Planning in Complex Warehouse Environment

Contact Info

Product

Resources

About