Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications
An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. W...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9466498/ |
id |
doaj-78325d7dd91b4fc7a790955c8fed6f19 |
---|---|
record_format |
Article |
spelling |
doaj-78325d7dd91b4fc7a790955c8fed6f192021-07-07T23:00:23ZengIEEEIEEE Access2169-35362021-01-019938319384610.1109/ACCESS.2021.30931139466498Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT ApplicationsSheetal N. Ghorpade0https://orcid.org/0000-0002-3292-8044Marco Zennaro1https://orcid.org/0000-0002-0578-0830Bharat S. Chaudhari2https://orcid.org/0000-0003-2401-8996Rashid A. Saeed3https://orcid.org/0000-0002-9872-081XHesham Alhumyani4S. Abdel-Khalek5RMD Sinhgad School of Engineering, Savitribai Phule Pune University, Pune, IndiaScience, Technology and Innovation Unit, The Abdus Salam International Centre for Theoretical Physics, Trieste, ItalySchool of Electronics and Communication Engineering, MIT World Peace University, Pune, IndiaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaAn optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process’s continuance and adding proactive and reactive features. The proposed algorithm’s performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application.https://ieeexplore.ieee.org/document/9466498/Convergencecrossover operatordifferential evolution operationInternet of Thingsoptimizationparticle swarm optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sheetal N. Ghorpade Marco Zennaro Bharat S. Chaudhari Rashid A. Saeed Hesham Alhumyani S. Abdel-Khalek |
spellingShingle |
Sheetal N. Ghorpade Marco Zennaro Bharat S. Chaudhari Rashid A. Saeed Hesham Alhumyani S. Abdel-Khalek Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications IEEE Access Convergence crossover operator differential evolution operation Internet of Things optimization particle swarm optimization |
author_facet |
Sheetal N. Ghorpade Marco Zennaro Bharat S. Chaudhari Rashid A. Saeed Hesham Alhumyani S. Abdel-Khalek |
author_sort |
Sheetal N. Ghorpade |
title |
Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications |
title_short |
Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications |
title_full |
Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications |
title_fullStr |
Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications |
title_full_unstemmed |
Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications |
title_sort |
enhanced differential crossover and quantum particle swarm optimization for iot applications |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process’s continuance and adding proactive and reactive features. The proposed algorithm’s performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application. |
topic |
Convergence crossover operator differential evolution operation Internet of Things optimization particle swarm optimization |
url |
https://ieeexplore.ieee.org/document/9466498/ |
work_keys_str_mv |
AT sheetalnghorpade enhanceddifferentialcrossoverandquantumparticleswarmoptimizationforiotapplications AT marcozennaro enhanceddifferentialcrossoverandquantumparticleswarmoptimizationforiotapplications AT bharatschaudhari enhanceddifferentialcrossoverandquantumparticleswarmoptimizationforiotapplications AT rashidasaeed enhanceddifferentialcrossoverandquantumparticleswarmoptimizationforiotapplications AT heshamalhumyani enhanceddifferentialcrossoverandquantumparticleswarmoptimizationforiotapplications AT sabdelkhalek enhanceddifferentialcrossoverandquantumparticleswarmoptimizationforiotapplications |
_version_ |
1721314556769206272 |