HEPSO: an efficient sensor node redeployment strategy based on hybrid optimization algorithm in UWASN

2021 ◽  
Author(s):  
Bhumika Gupta ◽  
Kamal Kumar Gola ◽  
Manish Dhingra
Keyword(s):  
Optimization Algorithm ◽  
Sensor Node ◽  
Hybrid Optimization ◽  
Hybrid Optimization Algorithm

Adaptive neural network with hybrid optimization oriented localization in wireless sensor network: A multi-objective model

2020 ◽  
pp. 2050064
Author(s):  
P. Purusothaman ◽  
M. Gunasekaran
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Sensor Node ◽  
Optimization Algorithms ◽  
Sensor Nodes ◽  
Hybrid Optimization ◽  
Wireless Sensor ◽  
Hybrid Optimization Algorithm ◽  
Localization Model ◽  
Anchor Nodes

The localization strategy is broadly utilized in Wireless Sensor Networks (WSNs) to detect the present location of the sensor nodes. A WSN comprises of multiple sensor nodes, which makes the employment of GPS on each sensor node costly, and GPS does not give accurate localization outcomes in an indoor environment. The process of configuring location reference on each sensor node manually is also not feasible in the case of a large dense network. Hence, this proposal plans to develop an intelligent model for developing localization pattern in WSN with a group of anchor nodes, rest nodes, and target nodes. The initial step of the proposed node localization model is the selection of the optimal location of anchor nodes towards the target nodes using the hybrid optimization algorithm by concerning the constraints like the distance between the nodes. The second step is to optimally determine the location of the rest node by reference to the anchor nodes using the same hybrid optimization algorithm. Here, the weight has to be determined for each anchor sensor node based on its Received Signal Strength (RSS), and RSS threshold value with the assistance of Neural Network. The hybrid optimization algorithms check the direction to where the concerned node has to be moved by merging the beneficial concepts of two renowned optimization algorithms named as Rider Optimization Algorithm (ROA), and Chicken Swarm Optimization Algorithm (CSO) to solve the localization problem in WSN. The newly developed hybrid algorithm is termed as Rooster Updated Attacker-based ROA (RUA-ROA). Finally, the comparative evaluation indicates a significant improvement in the proposed localization model by evaluating the convergence and statistical analysis.

Optimization Design of Drawbead in Drawing Tools of Autobody Cover Panel

2002 ◽  
Vol 124 (2) ◽  
pp. 278-285 ◽  
Author(s):  
Gang Liu ◽  
Zhongqin Lin ◽  
Youxia Bao
Keyword(s):  
Optimization Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Experimental Results ◽  
Hybrid Optimization ◽  
Force Model ◽  
Hybrid Optimization Algorithm ◽  
Design Plan ◽  
Formed Part ◽  
Restraining Force

In the tooling design of autobody cover panels, design of drawbead will affect the distribution of drawing restraining force along mouth of dies and the relative flowing velocity of the blank, and consequently, will affect the distributions of strain and thickness in a formed part. Therefore, reasonable design of drawbead is the key point of cover panels’ forming quality. An optimization design method of drawbead, using one improved hybrid optimization algorithm combined with FEM software, is proposed in this paper. First, we used this method to design the distribution of drawbead restraining force along the mouth of a die, then the actual type and geometrical parameters of drawbead could be obtained according to an improved drawbead restraining force model and the improved hybrid optimization algorithm. This optimization method of drawbead was used in designing drawing tools of an actual autobody cover panel, and an optimized drawbead design plan has been obtained, by which deformation redundancy was increased from 0% under uniform drawbead control to 10%. Plastic strain of all area of formed part was larger than 2% and the minimum flange width was larger than 10 mm. Therefore, not only better formability and high dent resistance were obtained, but also fine cutting contour line and high assembly quality could be obtained. An actual drawing part has been formed using the optimized drawbead, and the experimental results were compared with the simulating results in order to verify the validity of the optimized design plan. Good agreement of thickness on critical areas between experimental results and simulation results proves that the optimization design method of drawbead could be successfully applied in designing actual tools of autobody cover panels.

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