scholarly journals Performance and evaluation of energy optimization techniques for wireless body area networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Naveen Bilandi ◽  
Harsh Kumar Verma ◽  
Renu Dhir
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Relay Node ◽  
Body Area Networks ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Body Area ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

Abstract Background Wireless body area networks are created to retrieve and transmit human health information by using sensors on the human body. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important mechanisms is selecting the relay node, which can be modeled as an optimization problem. These days nature-inspired algorithms are being widely used to solve various optimization problems. With regard to this, this paper aims to compare the performance of the three most recent nature-inspired metaheuristic algorithms for solving the relay node selection problem. Results It has been found that the total energy consumption calculated using grey wolf optimization decreased by 23% as compared to particle swarm optimization and 16% compared to ant lion optimization. Conclusions The results suggest that grey wolf optimization is better than the other two techniques due to its social hierarchy and hunting behavior. The findings showed that, compared to well-known heuristics such as particle swarm optimization and ant lion optimization, grey wolf optimization was able to deliver extremely competitive results. Graphical Abstract

A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization

2021 ◽  
Vol 12 (2) ◽  
pp. 1-21
Author(s):  
Gokul Yenduri ◽  
Veeranjaneyulu Naralasetti
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Software Metrics ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Software Metric ◽  
New Variant ◽  
Conventional Models

Maintainability index (MI) is a software metric that offers measurements of the maintainability before release of the software by facilitating several substantial features of the system. In general, there is a common formula for determining the MI for all the software metrics to ensure the system's reliability. As it does not provide appropriate results regarding the reliability of the system, it is essential to focus on the next level of MI of software. Hence, this paper intends to allot an optimal weight and a constant to each software metric, which is optimized by grey wolf optimization (GWO). As a result, it can provide a new variant of MI by proposed enhanced model-GWO (EM-GWO). This optimized MI can ensure the efficiency of the respective software in such a way that it can provide an enhanced score from the system. Further, the proposed method is compared with conventional models such as enhanced model-generic algorithm (EM-GA), EM-particle swarm optimization (PSO), EM-ant bee colony (ABC), EM-differential evolution (DE), and EM-fire fly (FF), and the results are obtained.

scholarly journals Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

2020 ◽  
Vol 16 (9) ◽  
pp. 155014772094913
Author(s):  
Mohamed Elhoseny ◽  
R Sundar Rajan ◽  
Mohammad Hammoudeh ◽  
K Shankar ◽  
Omar Aldabbas
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Swarm Intelligence ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence–based approaches. This article presents a novel swarm intelligence–based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm–based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization–grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization–grey wolf optimization technique under all the test cases.

scholarly journals Economic Load Dispatch

2021 ◽  
Vol 9 (VI) ◽  
pp. 1646-1651
Author(s):  
Prof. Kanika Lamba
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

ELD or Economic load dispatch is an online process of allocating generating among the available generating units to minimize the total generating cost and satisfy the equality and inequality constraint. ELD means the real and reactive power of the generator vary within the certain limits and fulfils theload demand with less fuel cost. There are some traditional methods for = 1; 2; :::;N) isgiven as Vi=[Vi;1; Vi;2; :::; Vi;D]. The index ivaries from solving ELD include lambda irritation method, Newton-Raphson method, Gradient method, etc. All these traditional algorithms need the incremental fuel cost curves of the generators to be increasing monotonically or piece-wise linear. But in practice the input-output characteristics of a generator are highly non-linear leading to a challenging non-convex optimization problem. Methods like artificial intelligence, DP (dynamic programming), GA (genetic algorithms), and PSO (particle swarm optimization), ALO ( ant-lion optimization), solve non convex optimization problems in an efficient manner and obtain a fast and near global and optimum solution. In this project ELD problem has been solved using Lambda-Iterative technique, ALO (ant-lion Optimization) and PSO (Particle Swarm Optimization) and the results have been compared. All the analyses have been made in MATLAB environment

scholarly journals An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Wei ◽  
Ni Ni ◽  
Dayou Liu ◽  
Huiling Chen ◽  
Mingjing Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Sensitivity And Specificity ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Support Vector ◽  
Final Decision ◽  
Feature Subset ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization

In order to develop a new and effective prediction system, the full potential of support vector machine (SVM) was explored by using an improved grey wolf optimization (GWO) strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction. In the proposed approach, particle swarm optimization (PSO) was firstly adopted to generate the diversified initial positions, and then GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on SVM. The resultant methodology, IGWO-SVM, is rigorously examined based on the real-life data which includes a series of factors that influence the students’ final decision to choose the specific major. To validate the proposed method, other metaheuristic based SVM methods including GWO based SVM, genetic algorithm based SVM, and particle swarm optimization-based SVM were used for comparison in terms of classification accuracy, AUC (the area under the receiver operating characteristic (ROC) curve), sensitivity, and specificity. The experimental results demonstrate that the proposed approach can be regarded as a promising success with the excellent classification accuracy, AUC, sensitivity, and specificity of 87.36%, 0.8735, 85.37%, and 89.33%, respectively. Promisingly, the proposed methodology might serve as a new candidate of powerful tools for second major selection.

Sign in / Sign up

Export Citation Format

Share Document