scholarly journals Power Allocation for 5G Mobile Multiuser Cooperative Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fagen Yin ◽  
Wencai Du
Keyword(s):  
Power Allocation ◽  
Communication Technology ◽  
Optimization Problem ◽  
Cooperative Networks ◽  
System Efficiency ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Decode And Forward ◽  
Fifth Generation ◽  
Multiuser Networks

With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.

scholarly journals Performance Comparison of Multi Design Method and Meta-Heuristic Methods for Optimal Preliminary Design of Core-Form Power Transformers

2017 ◽  
Vol 61 (1) ◽  
pp. 69-76 ◽  
Author(s):  
Tamás Orosz ◽  
Bence Borbély ◽  
Zoltán Ádám Tamus
Keyword(s):  
Optimization Problem ◽  
Power Transformer ◽  
Design Method ◽  
Power Transformers ◽  
Design Stage ◽  
Preliminary Design ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Three Phase ◽  
Type Power

Large power transformers are regarded as crucial and expensive assets in power systems. Due to the competing global market, to make a good and competing power transformer design, a non-linear optimization problem should be solved in a very short time in the preliminary design stage. The paper shows and compares the performance of four different methods to solve this problem for three phase core type power transformers. The first algorithm is a novel meta-heuristic technique which combines the geometric programming with the method of branch and bound. Then this conventional multi design method is solved by a simple iterative technique and two novel evolutionary algorithms to enhance the convergence speed. One of these algorithms is the particle swarm optimization technique which is used by many other researchers and the grey wolf optimization algorithm which is a new method in this optimization sub-problem. An example design on an 80 MVA, three phase core type power transformer using these four methods is presented and its performances are analyzed. The results demonstrate that the grey wolf optimization is a good alternative for this optimization problem.

scholarly journals Joint TAS and Power Allocation for SDF Relaying M2M Cooperative Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Lingwei Xu ◽  
Hao Zhang ◽  
Jingjing Wang
Keyword(s):  
Power Allocation ◽  
Fading Channels ◽  
Performance Optimization ◽  
Antenna Selection ◽  
Cooperative Networks ◽  
Transmit Antenna Selection ◽  
Decode And Forward ◽  
Simulation Results ◽  
Nakagami Fading Channels ◽  
Multiple Relay

The outage probability (OP) performance of multiple-relay-based selective decode-and-forward (SDF) relaying mobile-to-mobile (M2M) networks with transmit antenna selection (TAS) overN-Nakagami fading channels is investigated. The exact closed-form expressions for OP of the optimal and suboptimal TAS schemes are derived. The power allocation problem is formulated for performance optimization. Then, the OP performance under different conditions is evaluated through numerical simulations to verify the analysis. The simulation results showed that optimal TAS scheme has a better OP performance than suboptimal TAS scheme. Further, the power allocation parameter has an important influence on the OP performance.

scholarly journals Improved grey wolf algorithm for optimization problems

2021 ◽  
Vol 22 (3) ◽  
pp. 1573
Author(s):  
Hafiz Maaz Asgher ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali ◽  
Muhammad Aamir
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Research Work ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Exploration And Exploitation ◽  
Grey Wolf Optimization ◽  
Whale Optimization

The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Optimal Conductor Selection Using Grey Wolf Optimization

2016 ◽  
Vol 4 (3) ◽  
pp. 39
Author(s):  
Ramanaiah M. LAXMIDEVI ◽  
REDDY M. DAMODAR ◽  
◽  
Keyword(s):  
Grey Wolf ◽  
Grey Wolf Optimization
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