scholarly journals Multi-objective flower pollination algorithm applied to 5G vehicular networks communication

2022 ◽  
Vol 11 (1) ◽  
pp. e33911125020
Author(s):  
Francisco Jonatas Siqueira Coelho ◽  
Eulogio Gutierrez Huampo ◽  
Henrique Figueirôa Lacerda ◽  
Arthur Doria Meneses de Freitas ◽  
Abel Guilhermino da Silva Filho
Keyword(s):  
Millimeter Waves ◽  
Vehicular Networks ◽  
Flower Pollination Algorithm ◽  
Media Access Control ◽  
Vehicular Communication ◽  
Genetic Classification ◽  
Nsga Ii ◽  
Multi Objective ◽  
Flower Pollination ◽  
Differential Evolutionary Algorithm

The Cellular Vehicle-to-Everything (C-V2X) technology, as a widest version of Vehicular Ad-hoc Network (VANET), aims to interconnect vehicles and any other latest technological infrastructures. In this context, the fifth generation of mobile networks (5G) based on millimeter waves (mmWave) is an excellent alternative for the implementation of vehicular networks, mainly because it is capable of providing high data rates (Gbps) and ultra-low latency, requirements of C-V2X. On the other hand, mmWave signals are highly susceptible to blocking, causing low quality of service (QoS) in VANETs, compromising network functionality and the safety of drivers and pedestrians. Thus, in this work evolutionary computing techniques are applied in the simulation of a 5G vehicular network based on millimeter waves, exploring Media Access Control (MAC) sublayer parameters to optimize packet loss, latency and throughput, in order to optimize inter-vehicular communication. The Multi-objective Flower Pollination Algorithm (MOFPA) was used for this purpose. The results obtained show that the adopted approach can reach results close to the optimal pareto of non-dominated solutions, with a 75% reduction in exploration time in relation to the exhaustive search process. Finally, the performance of the metaheuristics adopted is compared with the non-dominated genetic classification algorithm (NSGA-II) and the multi-objective differential evolutionary algorithm (MODE).

scholarly journals Strategic Position and Tuning of UPFC using Multiple Indices and Flower pollination Algorithm for Contingency Management

2019 ◽  
Vol 9 (2S3) ◽  
pp. 154-159
Keyword(s):  
Transmission Line ◽  
Objective Function ◽  
Contingency Management ◽  
Flower Pollination Algorithm ◽  
Multi Objective ◽  
Flower Pollination ◽  
Strategic Position ◽  
Active Generation ◽  
Bus System

In this paper, a technique was proposed in the presence of UPFC to optimize the sizing of generators with Flower Pollination algorithm. The UPFC is based on an index incorporating both the L-index and the LUF index. For tuning the generators, a multi objective function has been selected. The multi-objective feature consists of deviation of voltage, cost of active generation of power and loss of transmission line. This approach was tested and implemented for regular loading and extreme network conditions due to line failure (contingency situation) on an IEEE 30 test bus system

Nature-Inspired Algorithms for Bi-Criteria Parallel Machine Scheduling

2019 ◽  
pp. 122-148
Author(s):  
Kawal Jeet
Keyword(s):  
Parallel Machines ◽  
Cuckoo Search ◽  
Parallel Machine Scheduling ◽  
Flower Pollination Algorithm ◽  
Grey Wolf Optimizer ◽  
Nsga Ii ◽  
Local Optima ◽  
Flower Pollination ◽  
Bee Colony ◽  
Nature Inspired Algorithms

Nature has always been a source of inspiration for human beings. Nature-inspired search-based algorithms have an enormous computational intelligence and capabilities and are observing diverse applications in engineering and manufacturing problems. In this chapter, six nature-inspired algorithms, namely artificial bee colony, bat, black hole, cuckoo search, flower pollination, and grey wolf optimizer algorithms, have been investigated for scheduling of multiple jobs on multiple potential parallel machines. Weighted flow time and tardiness have been used as optimization criteria. These algorithms are very efficient in identifying optimal solutions, but as the size of the problem increases, these algorithms tend to get stuck at local optima. In order to extract these algorithms from local optima, genetic algorithm has been used. Flower pollination algorithm, when appended with GA, is observed to perform better than other counterpart nature-inspired algorithms as well as existing heuristics and meta-heuristics based on MOGA and NSGA-II algorithms.

EEG-based Person Authentication Using Multi-objective Flower Pollination Algorithm

2018 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Joao P. Papa ◽  
Osama ahmad Alomari
Keyword(s):  
Flower Pollination Algorithm ◽  
Multi Objective ◽  
Flower Pollination

scholarly journals EEG Feature Extraction for Person Identification Using Wavelet Decomposition and Multi-Objective Flower Pollination Algorithm

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76007-76024 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Joao P. Papa ◽  
Osama Ahmad Alomari
Keyword(s):  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Flower Pollination Algorithm ◽  
Person Identification ◽  
Multi Objective ◽  
Flower Pollination

Multi-objective flower pollination algorithm: a new technique for EEG signal denoising

2022 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Xin-She Yang ◽  
Mazin Abed Mohammed ◽  
...  
Keyword(s):  
New Technique ◽  
Flower Pollination Algorithm ◽  
Signal Denoising ◽  
Eeg Signal ◽  
Multi Objective ◽  
Flower Pollination ◽  
A New Technique

scholarly journals Multi objective Flower Pollination Algorithm for solving capacitor placement in radial distribution system using data structure load flow analysis

2016 ◽  
Vol 65 (2) ◽  
pp. 203-220 ◽  
Author(s):  
V. Tamilselvan ◽  
T. Jayabarathi
Keyword(s):  
Data Structure ◽  
Radial Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Load Flow ◽  
Flower Pollination Algorithm ◽  
Radial Distribution System ◽  
Multi Objective ◽  
Flower Pollination ◽  
Reactive Current

Abstract The radial distribution system is a rugged system, it is also the most commonly used system, which suffers by loss and low voltage at the end bus. This loss can be reduced by the use of a capacitor in the system, which injects reactive current and also improves the voltage magnitude in the buses. The real power loss in the distribution line is the I2R loss which depends on the current and resistance. The connection of the capacitor in the bus reduces the reactive current and losses. The loss reduction is equal to the increase in generation, necessary for the electric power provided by firms. For consumers, the quality of power supply depends on the voltage magnitude level, which is also considered and hence the objective of the problem becomes the multi objective of loss minimization and the minimization of voltage deviation. In this paper, the optimal location and size of the capacitor is found using a new computational intelligent algorithm called Flower Pollination Algorithm (FPA). To calculate the power flow and losses in the system, novel data structure load flow is introduced. In this, each bus is considered as a node with bus associated data. Links between the nodes are distribution lines and their own resistance and reactance. To validate the developed FPA solutions standard test cases, IEEE 33 and IEEE 69 radial distribution systems are considered.

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