Multi-Objective Optimization of Stacking Sequences for Laminated Composite Beams by Genetic Algorithm

2015 ◽  
Vol 729 ◽  
pp. 89-94
Author(s):  
Fatih Karaçam ◽  
Taner Timarci
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Orientation Angle ◽  
Laminated Composite ◽  
Beam Theory ◽  
Composite Beams ◽  
Function Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective

In this study, multi-objective optimization of stacking sequences for laminated composite composite beams is studied for simply supported boundary conditions. A unified three-degrees-of-freedom shear deformable beam theory is used for analytical solution and genetic algorithm is used as optimization technique. By use of two different parameters such as the deflection and frequency together in a pre-defined fitness function, optimization process is carried out in order to maximize the fitness function. Initially, the deflection, frequency, fitness function values and corresponding stacking sequences are presented for various number of layers and increment of fiber orientation angle. The variation of the fitness function with respect to deflection and frequency depending on the number of generations are presented.

Hybrid Multi-Objective-Optimization Algorithm for Energy Efficient Priority-based QoS Routing in IoT networks

2021 ◽  
Author(s):  
THEN MOZHI
Keyword(s):  
Optimization Algorithm ◽  
Energy Efficient ◽  
Fitness Function ◽  
Optimization Technique ◽  
Qos Routing ◽  
Traffic Load ◽  
Delivery Ratio ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
End To End

The growing requirement for real-time Internet of Things (IoT) applications has ended with Quality of Service (QoS) communication protocols. where heterogeneous IoT data collection and communication processing contains specific requirements in terms of energy, reliability, latency, and priority. Due to energy constraints, a proper estimation model for monitoring and control is accomplished by the objective of sensing and end-to-end communication respectively. moreover, the connectivity requires a QoS routing protocol to finding the route selection for sensor networks. Hence, data routing and prioritization and Satisfying the QoS requirements are the significant challenges in such networks. So for the Multi-objective Optimization for QoS Routing method is used for differentiating the traffics while data communication and gives the requirements to be caring about the network resource. In this paper, the Energy-Efficient Priority-based Multi-Objective QoS routing (PMQoSR) mechanism ensures the energy and Qos in IoT networks. the proposed system regulates the routing performance based on the QoS parameters, using optimization technique for three hybrid algorithms, named as WLFA- Whale Lion Fireworks optimization algorithm with Fitness Function Routing(FFR) mechanisms .the WLFA to prevent congestion and minimizes the localization error using and select the shortest routing path through the network period uses Priority label and time delay patterns when sending data to the destination. We evaluate its performance and existing competing schemes in terms of Energy-Efficient. The results demonstrate that PMQoSR holds out considering network traffic, packets forwarding, error rate, energy, and distance between the nodes and also considers priority-aware routing to improve the traffic load, throughput, end-to-end delay, and packet delivery ratio when compared with the existing systems.

scholarly journals Multi-objective optimization of magnetic abrasive finishing by desirability function with genetic algorithm

2021 ◽  
Vol 13 (1) ◽  
pp. 1-9
Author(s):  
Rajneesh Kumar Singh ◽  
Swati Gangwar ◽  
D.K. Singh ◽  
Shadab Ahmad
Keyword(s):  
Genetic Algorithm ◽  
Function Analysis ◽  
Desirability Function ◽  
Fitness Function ◽  
Hybrid Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Finished Surface ◽  
En 31 ◽  
Desirability Function Analysis

Thermal stability and Surface hardness of the super-finished surface is a very important aspect to preserve the surface texture of workpiece in the MAF process. In this present study, the multi-objective optimization of EN-31 finished through the MAF process. “Increase in Temperature” and “Increase in Hardness” are considered for optimization to diminish their impact on the super-finished surface of EN-31. In present work Desirability function analysis (DFA) has been used to optimize the desired responses of the MAF process. Experiments were designed according to Taguchi L9 orthogonal array for the finishing of EN-31. The experiment results are processed using DFA and Desirability fitness function is established to convert the single response to multi-response. Genetic Algorithm (GA) is used to enhance the results of DFA and the regression model was developed to obtain the objective function of Genetic algorithm. Smaller-the-best criteria were used for ‘Increase in Temperature’ and ‘Increase in Hardness’ for obtaining favorable process parameters. The best optimal parametric combination is obtained by using the GA-DFA hybrid approach is at 2.5 mm (working gap), 20 gm (abrasive weight), and 2.0 A (Current) and 300 rpm (rotational speed).  

MULTI-OBJECTIVE OPTIMIZATION OF OFFSHORE TSUNAMI OBSERVATION POINTS BY GENETIC ALGORITHM

2020 ◽  
Vol 76 (2) ◽  
pp. I_325-I_330
Author(s):  
Kazutoshi KURAMOTO ◽  
Fumiyasu MAKINOSHIMA ◽  
Anawat SUPPASRI ◽  
Fumihiko IMAMURA
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective
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