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 Power Loss Calculation in 11-Level Cascaded H-Bridge Inverter using Harmony Search Optimized Switching Scheme

2019 ◽  
Vol 8 (6) ◽  
pp. 5200-5205
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
System Analysis ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Harmony Search ◽  
Multilevel Inverter ◽  
Total Power ◽  
Multi Objective Optimization ◽  
Multi Objective

Power loss is the most significant parameter in power system analysis and its adequate calculation directly effects the economic and technical evaluation. This paper aims to propose a multi-objective optimization algorithm which optimizes dc source magnitudes and switching angles to yield minimum THD in cascaded multilevel inverters. The optimization algorithm uses metaheuristic approach, namely Harmony Search algorithm. The effectiveness of the multi-objective algorithm has been tested with 11-level Cascaded H-Bridge Inverter with optimized DC voltage sources using MATLAB/Simulink. As the main objective of this research paper is to analyze total power loss, calculations of power loss are simplified using approximation of curves from datasheet values and experimental measurements. The simulation results, obtained using multi-objective optimization method, have been compared with basic SPWM, optimal minimization of THD, and it is confirmed that the multilevel inverter fired using multi- objective optimization technique has reduced power loss and minimum THD for a wide operating range of multilevel inverter.

Cooperative Genetic Multi-Objective Optimization Algorithm and Application

2012 ◽  
Vol 220-223 ◽  
pp. 2814-2817
Author(s):  
Li Gao ◽  
Dan Kong
Keyword(s):  
Optimization Algorithm ◽  
Pareto Front ◽  
Fitness Function ◽  
Multi Objective Optimization ◽  
Basic Algorithm ◽  
Multi Objective ◽  
Complicated System ◽  
Present Solution

It is very difficult to find out the best solution for some complicated system problems frequently appear. These problems are mostly of multi-objective. The present solution, however, is short of communication. Based on CO, one of MDO method, this paper gives a new simple kind of multi-objective framework, which will be suitable to multi-subject problems. It can not only organize each disciplinary effectively, but gives the inter-influence between disciplinaries by fitness function as well. Meanwhile, the perfect NSGAⅡ is used as be the basic algorithm, prematurity can be avoided and Pareto front with good distribution is obtained. Micro machined accelerometer example validates the correctness of the framework.

scholarly journals Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

2021 ◽  
Author(s):  
Duraimurugan Samiayya ◽  
Avudaiammal Ramalingam
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Fitness Function ◽  
Heavy Traffic ◽  
Spider Monkey ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Multi Objective ◽  
Spider Monkey Optimization

Abstract In wireless sensor network (WSN), the gateways far away from the base station (BS) uses the gateways nearer to the BS to forward the data. It causes heavy traffic to the gateways in proximity with the BS. They need to manage this heavy traffic load but it leads to additional energy consumption and reduction in network lifetime. In order to overcome these issues, loads around the gateways need to be balanced. In this paper, multi objective based spider monkey optimization (MOSMO) has been presented to balance the load and to improve the network lifetime through energy efficient routing and clustering. The objective functions such as routing fitness and clustering fitness have been considered for optimal routing and clustering. The routing fitness function is found by incorporating both the minimum distance traversed by the gateways and minimum number of the gateway hops. The clustering fitness function is the minimum fitness function of gateways. The fitness function of each gateway is computed based on both the mean load of gateways as well as the distance between gateways and BS. The performance of the proposed MOSMO based routing and clustering scheme is compared with the existing Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) based routing and clustering scheme. The QoS features such as delay, energy consumption, delivery ratio, throughput and network lifetime with various node density are analyzed. The proposed work is simulated using MATLAB. The results show that, the reduction in delay and energy consumption is about 18% and 17% respectively whereas improvement in delivery ratio, throughput and network life time is about 15%, 24% and 19% respectively when compared to the existing PSO and GWO methods.

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.

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