This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-toimplement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straightforward evolutionary algorithm that can compete with other well-known advanced methods.
This paper proposes an Ant Colony Optimization (ACO) approach to solve a variant of the Traveling Salesman Problem called the Random Traveling Salesman Problem (RTSP). In the RTSP, city coordinates are randomly generated rather than using predefined datasets. The ACO model is implemented in MATLAB and tested on randomly generated RTSP datasets ranging from 10 to 200 cities. Results show that the ACO approach finds acceptable solutions and performs well for smaller problem sizes, but performance degrades as the problem size increases. The paper concludes that ACO has potential for solving optimization problems if applied properly.
Embellished Particle Swarm Optimization Algorithm for Solving Reactive Power ...ijeei-iaes
This document summarizes a research paper that proposes an Embellished Particle Swarm Optimization (EPSO) algorithm to solve the reactive power problem in power systems. EPSO extends the standard PSO algorithm by dividing particles into multiple interacting swarms to maintain diversity. It is tested on standard IEEE 57-bus and 118-bus test systems and shown to reduce real power losses more effectively than other algorithms. The EPSO approach cooperatively updates particles between swarms to converge faster to near-optimal solutions while avoiding premature convergence.
A genetic algorithm for the optimal design of a multistage amplifier IJECEIAES
The optimal sizing of analog circuits is one of the most complicated processes, because of the number of variables taken into, to the number of required objectives to be optimized and to the constraint functions restrictions. The aim is to automate this activity in order to accelerate the circuits design and sizing. In this paper, we deal with the optimization of the three stage bipolar transistor amplifier performances namely the voltage gain (AV), the input impedance (ZIN), the output impedance (ZOUT), the power consumption (P) and the low and the high cutoff frequency (FL,FH), through the Genetic Algorithm (GA). The presented optimization problem is of multi-dimensional parameters, and the trade-off of all parameters. In fact, the passive components (Resistors and Capacitors) are selected from manufactured constant values (E12, E24, E48, E96, E192) for the purpose of reduce the cost of design; also, the intrinsic parameters of transistors (hybrid parameters and the junction capacitances) are considered variables in order not to be limited in design. SPICE simulation is used to validate the obtained result/performances.
A hybrid bacterial foraging and modified particle swarm optimization for mode...IJECEIAES
This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
EFFECTS OF THE DIFFERENT MIGRATION PERIODS ON PARALLEL MULTI-SWARM PSOcscpconf
This document discusses the effects of different migration periods on the Parallel Multi-Swarm Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel metaheuristic algorithm based on Particle Swarm Optimization and the Comprehensive Learning Particle Swarm Optimization. It uses multiple swarms that work cooperatively and concurrently. The migration period, which is the frequency at which swarms share information, is an important parameter. The document analyzes PCLPSO performance on benchmark functions using different migration periods, finding the best periods varied with problem dimension and type.
Effects of The Different Migration Periods on Parallel Multi-Swarm PSO csandit
In recent years, there has been an increasing inter
est in parallel computing. In parallel
computing, multiple computing resources are used si
multaneously in solving a problem. There
are multiple processors that will work concurrently
and the program is divided into different
tasks to be simultaneously solved. Recently, a cons
iderable literature has grown up around the
theme of metaheuristic algorithms. Particle swarm o
ptimization (PSO) algorithm is a popular
metaheuristic algorithm. The parallel comprehensive
learning particle swarm optimization
(PCLPSO) algorithm based on PSO has multiple swarms
based on the master-slave paradigm
and works cooperatively and concurrently. The migra
tion period is an important parameter in
PCLPSO and affects the efficiency of the algorithm.
We used the well-known benchmark
functions in the experiments and analysed the perfo
rmance of PCLPSO using different
migration periods.
In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort into the design of a micro strip patch antennas. Nowadays Evolutionary Computation has its growth to extent. Generally electromagnetic optimization problems generally involve a large number of parameters. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest.
The document describes an improved particle swarm optimization algorithm to solve vehicle routing problems. It introduces concepts of leptons and hadrons to particles in the algorithm. Leptons interact weakly based on individual and neighborhood best positions, while hadrons (local best particles) undergo strong interactions by colliding with the global best particle. When stagnation occurs, particle decay is used to increase diversity. Simulations show the improved algorithm avoids premature convergence and finds better solutions compared to the basic particle swarm optimization.
This paper proposes an Ant Colony Optimization (ACO) approach to solve a variant of the Traveling Salesman Problem called the Random Traveling Salesman Problem (RTSP). In the RTSP, city coordinates are randomly generated rather than using predefined datasets. The ACO model is implemented in MATLAB and tested on randomly generated RTSP datasets ranging from 10 to 200 cities. Results show that the ACO approach finds acceptable solutions and performs well for smaller problem sizes, but performance degrades as the problem size increases. The paper concludes that ACO has potential for solving optimization problems if applied properly.
Embellished Particle Swarm Optimization Algorithm for Solving Reactive Power ...ijeei-iaes
This document summarizes a research paper that proposes an Embellished Particle Swarm Optimization (EPSO) algorithm to solve the reactive power problem in power systems. EPSO extends the standard PSO algorithm by dividing particles into multiple interacting swarms to maintain diversity. It is tested on standard IEEE 57-bus and 118-bus test systems and shown to reduce real power losses more effectively than other algorithms. The EPSO approach cooperatively updates particles between swarms to converge faster to near-optimal solutions while avoiding premature convergence.
A genetic algorithm for the optimal design of a multistage amplifier IJECEIAES
The optimal sizing of analog circuits is one of the most complicated processes, because of the number of variables taken into, to the number of required objectives to be optimized and to the constraint functions restrictions. The aim is to automate this activity in order to accelerate the circuits design and sizing. In this paper, we deal with the optimization of the three stage bipolar transistor amplifier performances namely the voltage gain (AV), the input impedance (ZIN), the output impedance (ZOUT), the power consumption (P) and the low and the high cutoff frequency (FL,FH), through the Genetic Algorithm (GA). The presented optimization problem is of multi-dimensional parameters, and the trade-off of all parameters. In fact, the passive components (Resistors and Capacitors) are selected from manufactured constant values (E12, E24, E48, E96, E192) for the purpose of reduce the cost of design; also, the intrinsic parameters of transistors (hybrid parameters and the junction capacitances) are considered variables in order not to be limited in design. SPICE simulation is used to validate the obtained result/performances.
A hybrid bacterial foraging and modified particle swarm optimization for mode...IJECEIAES
This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
EFFECTS OF THE DIFFERENT MIGRATION PERIODS ON PARALLEL MULTI-SWARM PSOcscpconf
This document discusses the effects of different migration periods on the Parallel Multi-Swarm Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel metaheuristic algorithm based on Particle Swarm Optimization and the Comprehensive Learning Particle Swarm Optimization. It uses multiple swarms that work cooperatively and concurrently. The migration period, which is the frequency at which swarms share information, is an important parameter. The document analyzes PCLPSO performance on benchmark functions using different migration periods, finding the best periods varied with problem dimension and type.
Effects of The Different Migration Periods on Parallel Multi-Swarm PSO csandit
In recent years, there has been an increasing inter
est in parallel computing. In parallel
computing, multiple computing resources are used si
multaneously in solving a problem. There
are multiple processors that will work concurrently
and the program is divided into different
tasks to be simultaneously solved. Recently, a cons
iderable literature has grown up around the
theme of metaheuristic algorithms. Particle swarm o
ptimization (PSO) algorithm is a popular
metaheuristic algorithm. The parallel comprehensive
learning particle swarm optimization
(PCLPSO) algorithm based on PSO has multiple swarms
based on the master-slave paradigm
and works cooperatively and concurrently. The migra
tion period is an important parameter in
PCLPSO and affects the efficiency of the algorithm.
We used the well-known benchmark
functions in the experiments and analysed the perfo
rmance of PCLPSO using different
migration periods.
In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort into the design of a micro strip patch antennas. Nowadays Evolutionary Computation has its growth to extent. Generally electromagnetic optimization problems generally involve a large number of parameters. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest.
The document describes an improved particle swarm optimization algorithm to solve vehicle routing problems. It introduces concepts of leptons and hadrons to particles in the algorithm. Leptons interact weakly based on individual and neighborhood best positions, while hadrons (local best particles) undergo strong interactions by colliding with the global best particle. When stagnation occurs, particle decay is used to increase diversity. Simulations show the improved algorithm avoids premature convergence and finds better solutions compared to the basic particle swarm optimization.
With the widespread of smart mobile devices and the
availability of many applications that provide maps, many programs
have spread to find the closest and fastest routes between
two points on the map. While the exactness and effectiveness of
best path depend on the traffic circumstances, the system needs to
add more parameters such as real traffic density and velocity in
road. In addition, because of the restricted resources of phone devices,
it is not reasonable to be used to calculate the exact optimal
solutions by some familiar deterministic algorithms, which are
usually used to find the shortest path with a map of reasonable
node number. To resolve this issue, this paper put forward to use
the genetic algorithm to reduce the computational time. The proposed
system use the genetic algorithm to find the shortest path
time with miscellaneous situations of real traffic conditions. The
genetic algorithm is clearly demonstrate excellent result when applied
on many types of map, especially when the number of nodes
increased.
ANALYSINBG THE MIGRATION PERIOD PARAMETER IN PARALLEL MULTI-SWARM PARTICLE SW...ijcsit
This document analyzes the effect of different migration periods on the Parallel Comprehensive Learning Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel multi-swarm algorithm based on Particle Swarm Optimization (PSO) and Comprehensive Learning PSO (CLPSO). It uses multiple swarms that work cooperatively and concurrently. The migration period determines how often each swarm shares its best solution with other swarms, and affects the algorithm's efficiency. The document tests PCLPSO on 14 benchmark optimization functions using different migration periods, to analyze their impact on the algorithm's performance.
1) A PID controller was designed using ISE, IAE, ITAE and MSE error criteria for a stable linear time invariant continuous system. A bacterial foraging particle swarm optimization (BF-PSO) technique was used to tune the PID controller gains (Kp, Ki, Kd) to meet performance specifications.
2) The PID controller was applied to the system using each error criteria and the closed-loop responses were observed and compared. For the IAE criteria, the system had 0% overshoot and undershoot with a settling time of 7.2172e-006 seconds and rise time of 4.0551e-006 seconds.
3) The BF-PSO algorithm combines
This document describes using a hybrid bacterial foraging particle swarm optimization (BF-PSO) algorithm to tune the parameters of a PID controller (Kp, Ki, Kd) for a stable linear time invariant system. The BF-PSO algorithm combines bacterial foraging optimization and particle swarm optimization to find optimal PID parameters by minimizing error criteria like ISE, IAE, ITAE and MSE. The transfer function of the plant is given and the PID controller is tuned using BF-PSO to minimize these performance indices and improve the closed loop step response.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
nternational Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Radio-frequency circular integrated inductors sizing optimization using bio-...IJECEIAES
In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.
This document summarizes the bat algorithm, which is a metaheuristic optimization algorithm inspired by the echolocation behavior of microbats. It describes how bats use echolocation to locate prey and obstacles. The basic steps of the bat algorithm are outlined, including how bats emit calls and adjust properties like frequency and loudness. Variants of the bat algorithm are mentioned for solving multi-objective, fuzzy logic, and other problems. Applications discussed include engineering design, scheduling, data clustering, and image processing. Advantages include quick convergence and flexibility, while disadvantages include possible stagnation if parameters are adjusted too rapidly.
AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...ijaia
In this paper, an improved multimodal optimization (MMO) algorithm,calledLSEPSO,has been proposed. LSEPSO combinedElectrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then madesome modification onthem. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which usedn-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it triedto find a point which could be the alternative of particle's personal best. This methodprevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstratedthat the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.
Hybrid Meta-Heuristic Algorithms For Solving Network Design ProblemAlana Cartwright
This document discusses hybrid meta-heuristic algorithms for solving network design problems. It proposes hybridizing the ant system meta-heuristic with genetic algorithms, simulated annealing and tabu search. Seven hybrid algorithms are developed and tested on the Sioux Falls network, finding the hybrids to be more effective than the base ant system alone. One hybrid combining all four concepts is also applied to a real network of a city with over 2 million people, proving more effective than the base network.
Ant Colony Optimization for Optimal Low-Pass State Variable Filter Sizing IJECEIAES
In analog filter design, discrete components values such as resistors (R) and capacitors (C) are selected from the series following constant values chosen. Exhaustive search on all possible combinations for an optimized design is not feasible. In this paper, we present an application of the Ant Colony Optimization technique (ACO) in order to selected optimal values of resistors and capacitors from different manufactured series to satisfy the filter design criteria. Three variants of the Ant Colony Optimization are applied, namely, the AS (Ant System), the MMAS (Min-Max AS) and the ACS (Ant Colony System), for the optimal sizing of the Low-Pass State Variable Filter. SPICE simulations are used to validate the obtained results/performances which are compared with already published works.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that
artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.
Routing in Wireless Mesh Networks: Two Soft Computing Based Approachesijmnct
Due to dynamic network conditions, routing is the most critical part in WMNs and needs to be optimised.
The routing strategies developed for WMNs must be efficient to make it an operationally self configurable
network. Thus we need to resort to near shortest path evaluation. This lays down the requirement of some
soft computing approaches such that a near shortest path is available in an affordable computing time. This
paper proposes a Fuzzy Logic based integrated cost measure in terms of delay, throughput and jitter.
Based upon this distance (cost) between two adjacent nodes we evaluate minimal shortest path that updates
routing tables. We apply two recent soft computing approaches namely Big Bang Big Crunch (BB-BC) and
Biogeography Based Optimization (BBO) approaches to enumerate shortest or near short paths. BB-BC
theory is related with the evolution of the universe whereas BBO is inspired by dynamical equilibrium in
the number of species on an island. Both the algorithms have low computational time and high convergence
speed. Simulation results show that the proposed routing algorithms find the optimal shortest path taking
into account three most important parameters of network dynamics. It has been further observed that for
the shortest path problem BB-BC outperforms BBO in terms of speed and percent error between the
evaluated minimal path and the actual shortest path.
Abstract:Reactive Power Optimization is a complex combinatorial optimization problem involving non-linear function having multiple local minima, non-linear and discontinuous constrains. This paper presents PS2O, which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybrid Evolutionary-Conventional Algorithm (HECA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GSO), combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said algorithms is used to overcome the Problem of premature convergence. PS2O, HECA , GSO is applied to Reactive Power Optimization problem and is evaluated on standard IEEE 57, practical 191 test Bus Systems. The results shows that all the three algorithms perform well in solving the reactive power problem and prevent premature convergence to high degree but still keep a rapid convergence. Of all the three PS2O has the edge in reducing the real power loss when compared to HECA & GSO.
Keywords:PS2O, Hybrid Evolutionary-Conventional Algorithm, Genetical Swarm Optimization, Reactive Power Optimization.
Determining optimal location and size of capacitors in radial distribution ne...IJECEIAES
In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.
Efficient steganography techniques are needed for the security of digital information over the Internet and for secret data communication. Therefore, many techniques are proposed for steganography. One of these intelligent techniques is Particle Swarm Optimization (PSO) algorithm. Recently, many modifications are made to Standard PSO (SPSO) such as Human-Based Particle Swarm Optimization (HPSO). Therefore, this paper presents image steganography using HPSO in order to find best locations in image cover to hide text secret message. Then, a comparison is done between image steganography using PSO and using HPSO. Experimental results on six (256×256) cover images and different size of secret massages, prove that the performance of the proposed image steganography using HPSO has been improved in comparison with using SPSO.
An optimal design of current conveyors using a hybrid-based metaheuristic alg...IJECEIAES
This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.
An improved ant colony algorithm based onIJCI JOURNAL
This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial
optimization problems. The existing algorithms still have some imperfections, we use a combination of two
different operators to improve the performance of algorithm in this work. First, 3-opt local search is used
as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is
proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into
local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude
that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the
local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions.
Therefore, it’s better and more effective algorithm for TSP.
Analysis of Four-Bar Linkages Model using Regressioninventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
More Related Content
Similar to Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm
With the widespread of smart mobile devices and the
availability of many applications that provide maps, many programs
have spread to find the closest and fastest routes between
two points on the map. While the exactness and effectiveness of
best path depend on the traffic circumstances, the system needs to
add more parameters such as real traffic density and velocity in
road. In addition, because of the restricted resources of phone devices,
it is not reasonable to be used to calculate the exact optimal
solutions by some familiar deterministic algorithms, which are
usually used to find the shortest path with a map of reasonable
node number. To resolve this issue, this paper put forward to use
the genetic algorithm to reduce the computational time. The proposed
system use the genetic algorithm to find the shortest path
time with miscellaneous situations of real traffic conditions. The
genetic algorithm is clearly demonstrate excellent result when applied
on many types of map, especially when the number of nodes
increased.
ANALYSINBG THE MIGRATION PERIOD PARAMETER IN PARALLEL MULTI-SWARM PARTICLE SW...ijcsit
This document analyzes the effect of different migration periods on the Parallel Comprehensive Learning Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel multi-swarm algorithm based on Particle Swarm Optimization (PSO) and Comprehensive Learning PSO (CLPSO). It uses multiple swarms that work cooperatively and concurrently. The migration period determines how often each swarm shares its best solution with other swarms, and affects the algorithm's efficiency. The document tests PCLPSO on 14 benchmark optimization functions using different migration periods, to analyze their impact on the algorithm's performance.
1) A PID controller was designed using ISE, IAE, ITAE and MSE error criteria for a stable linear time invariant continuous system. A bacterial foraging particle swarm optimization (BF-PSO) technique was used to tune the PID controller gains (Kp, Ki, Kd) to meet performance specifications.
2) The PID controller was applied to the system using each error criteria and the closed-loop responses were observed and compared. For the IAE criteria, the system had 0% overshoot and undershoot with a settling time of 7.2172e-006 seconds and rise time of 4.0551e-006 seconds.
3) The BF-PSO algorithm combines
This document describes using a hybrid bacterial foraging particle swarm optimization (BF-PSO) algorithm to tune the parameters of a PID controller (Kp, Ki, Kd) for a stable linear time invariant system. The BF-PSO algorithm combines bacterial foraging optimization and particle swarm optimization to find optimal PID parameters by minimizing error criteria like ISE, IAE, ITAE and MSE. The transfer function of the plant is given and the PID controller is tuned using BF-PSO to minimize these performance indices and improve the closed loop step response.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
nternational Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Radio-frequency circular integrated inductors sizing optimization using bio-...IJECEIAES
In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.
This document summarizes the bat algorithm, which is a metaheuristic optimization algorithm inspired by the echolocation behavior of microbats. It describes how bats use echolocation to locate prey and obstacles. The basic steps of the bat algorithm are outlined, including how bats emit calls and adjust properties like frequency and loudness. Variants of the bat algorithm are mentioned for solving multi-objective, fuzzy logic, and other problems. Applications discussed include engineering design, scheduling, data clustering, and image processing. Advantages include quick convergence and flexibility, while disadvantages include possible stagnation if parameters are adjusted too rapidly.
AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...ijaia
In this paper, an improved multimodal optimization (MMO) algorithm,calledLSEPSO,has been proposed. LSEPSO combinedElectrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then madesome modification onthem. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which usedn-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it triedto find a point which could be the alternative of particle's personal best. This methodprevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstratedthat the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.
Hybrid Meta-Heuristic Algorithms For Solving Network Design ProblemAlana Cartwright
This document discusses hybrid meta-heuristic algorithms for solving network design problems. It proposes hybridizing the ant system meta-heuristic with genetic algorithms, simulated annealing and tabu search. Seven hybrid algorithms are developed and tested on the Sioux Falls network, finding the hybrids to be more effective than the base ant system alone. One hybrid combining all four concepts is also applied to a real network of a city with over 2 million people, proving more effective than the base network.
Ant Colony Optimization for Optimal Low-Pass State Variable Filter Sizing IJECEIAES
In analog filter design, discrete components values such as resistors (R) and capacitors (C) are selected from the series following constant values chosen. Exhaustive search on all possible combinations for an optimized design is not feasible. In this paper, we present an application of the Ant Colony Optimization technique (ACO) in order to selected optimal values of resistors and capacitors from different manufactured series to satisfy the filter design criteria. Three variants of the Ant Colony Optimization are applied, namely, the AS (Ant System), the MMAS (Min-Max AS) and the ACS (Ant Colony System), for the optimal sizing of the Low-Pass State Variable Filter. SPICE simulations are used to validate the obtained results/performances which are compared with already published works.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that
artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.
Routing in Wireless Mesh Networks: Two Soft Computing Based Approachesijmnct
Due to dynamic network conditions, routing is the most critical part in WMNs and needs to be optimised.
The routing strategies developed for WMNs must be efficient to make it an operationally self configurable
network. Thus we need to resort to near shortest path evaluation. This lays down the requirement of some
soft computing approaches such that a near shortest path is available in an affordable computing time. This
paper proposes a Fuzzy Logic based integrated cost measure in terms of delay, throughput and jitter.
Based upon this distance (cost) between two adjacent nodes we evaluate minimal shortest path that updates
routing tables. We apply two recent soft computing approaches namely Big Bang Big Crunch (BB-BC) and
Biogeography Based Optimization (BBO) approaches to enumerate shortest or near short paths. BB-BC
theory is related with the evolution of the universe whereas BBO is inspired by dynamical equilibrium in
the number of species on an island. Both the algorithms have low computational time and high convergence
speed. Simulation results show that the proposed routing algorithms find the optimal shortest path taking
into account three most important parameters of network dynamics. It has been further observed that for
the shortest path problem BB-BC outperforms BBO in terms of speed and percent error between the
evaluated minimal path and the actual shortest path.
Abstract:Reactive Power Optimization is a complex combinatorial optimization problem involving non-linear function having multiple local minima, non-linear and discontinuous constrains. This paper presents PS2O, which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybrid Evolutionary-Conventional Algorithm (HECA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GSO), combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said algorithms is used to overcome the Problem of premature convergence. PS2O, HECA , GSO is applied to Reactive Power Optimization problem and is evaluated on standard IEEE 57, practical 191 test Bus Systems. The results shows that all the three algorithms perform well in solving the reactive power problem and prevent premature convergence to high degree but still keep a rapid convergence. Of all the three PS2O has the edge in reducing the real power loss when compared to HECA & GSO.
Keywords:PS2O, Hybrid Evolutionary-Conventional Algorithm, Genetical Swarm Optimization, Reactive Power Optimization.
Determining optimal location and size of capacitors in radial distribution ne...IJECEIAES
In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.
Efficient steganography techniques are needed for the security of digital information over the Internet and for secret data communication. Therefore, many techniques are proposed for steganography. One of these intelligent techniques is Particle Swarm Optimization (PSO) algorithm. Recently, many modifications are made to Standard PSO (SPSO) such as Human-Based Particle Swarm Optimization (HPSO). Therefore, this paper presents image steganography using HPSO in order to find best locations in image cover to hide text secret message. Then, a comparison is done between image steganography using PSO and using HPSO. Experimental results on six (256×256) cover images and different size of secret massages, prove that the performance of the proposed image steganography using HPSO has been improved in comparison with using SPSO.
An optimal design of current conveyors using a hybrid-based metaheuristic alg...IJECEIAES
This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.
An improved ant colony algorithm based onIJCI JOURNAL
This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial
optimization problems. The existing algorithms still have some imperfections, we use a combination of two
different operators to improve the performance of algorithm in this work. First, 3-opt local search is used
as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is
proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into
local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude
that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the
local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions.
Therefore, it’s better and more effective algorithm for TSP.
Analysis of Four-Bar Linkages Model using Regressioninventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Similar to Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
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objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
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governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
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(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
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accuracy rate of 99.50%.
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This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
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172
several nature-inspired optimization methods on the design of symmetric switching CMOS inverter.
Specifically, PSO with constriction factor and inertia weight approach (PSOCFIWA), differential evolution
(DE), craziness based particle swarm optimization (CRPSO), hybrid harmony search with differential
evolution (HS-DE), PSO with aging leader and challenger (ALC-PSO), and firefly algorithm (FA) were
applied in [3-8], respectively. In each paper, it was shown that the used optimization method gives better
results than those presented in [1, 2] and those obtained using the real-coded genetic algorithm (RGA).
In this paper, the optimal transient behavior of CMOS inverter is investigated using a new
optimization method, the symbiotic organisms search (SOS) [9]. Specifically, SOS is used to find
the optimum values of the widths and lengths of the channels of the NMOS and PMOS transistors along with
the load capacitor that give symmetric switching characteristics of the inverter. Since its invention, SOS has
been used in several optimization problems in engineering. In [10, 11], SOS has been applied to solve
the optimal power flow and the economic emission load dispatch problems. Moreover, SOS has been
successfully applied in the optimal design of linear and circular antenna arrays [12, 13], in solving the load
frequency control problem [14], in structure optimization problems [15], in solving unconstrained function
optimization [16], in the design of an improved 3D Turbo Code [17], in the optimal design of analog active
filters [18], in the synthesis of elliptical antenna arrays [19], and other engineering areas [20-24]. The results
indicated that SOS gives very good results and is very competitive with the state of the art for the solution of
these problems, which motivated this work. The SOS results will be compared with those obtained using
different optimization methods available in the literature [3-7]. Moreover, SOS results will be compared with
those obtained by the PSPICE simulator.
The paper is divided as follows: SOS algorithm is briefly described in Section 2. The interested
reader may consult the above references for more details on the SOS algorithm. In Section 3, the formulation
of the problem is presented, while numerical results and discussion are presented in Section 4. Section 5
concludes the paper.
2. SOS ALGORITHM
Symbiotic Organisms Search (SOS) has been recently proposed in [9] as an effective evolutionary
global optimization method. It is based on the symbiotic interaction strategies between different organisms in
an ecosystem. Symbiosis describes the interaction between different species living together. In SOS, the three
main types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple
mathematical expressions. An example of mutualism, in which both organisms benefit from such
a relationship, is the interaction between bees and flowers. An example of commensalism, where one species
benefits from the interaction, while the other does not gain or lose anything, is the relationship between
remora fish and sharks. An example of parasitism, where the relationship benefits one species while the other
species is harmed, is the interaction between the plasmodium parasites and humans. One of the main
advantages in using SOS is the fact that it is free from any tuning parameters. In its implementation, SOS
requires only common controlling parameters like population size (number of organisms) and maximum
number of iterations.
Similar to most evolutionary optimization methods, the first step in SOS is to randomly generate
an initial population of organisms (which is called the ecosystem). The number of generated organisms N is
called the population size. Thus, the ecosystem is an NxD matrix, where D is the dimension of the problem
(i.e., the number of design variables). An organism (individual) Xi (i=1, …, N) within the ecosystem
(population) is a real-valued vector with D elements which represents a single possible solution to a specific
optimization problem. The second step is to associate each organism Xi of the ecosystem (i.e., each row in
the ecosystem matrix) with a certain value of the fitness function which imitates the degree of adaptation to
the desired objective. Afterwards, new solutions are created by simulating the above mentioned three
symbiotic interactions between the organisms in the ecosystem. Each organism in the ecosystem randomly
interacts with other organisms through all these three phases and new organisms are created which have
better fitness function value. This process is repeated until a specific termination criterion (e.g., maximum
number of iterations) is fulfilled. In each iteration, every organism Xi (i=1, …, N) goes through the following
three phases:
2.1. Mutualism phase
Firstly, another organism Xj is randomly selected to “mutually” interact with Xi. Then, Xi and Xj are
updated according to the following expressions [9]:
(1)
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(2)
where the (Mutual Vector) is the average between the two interacting organisms, i.e.,
(Mutual Vector) = ( Xi +Xj ) / 2.
In the above equations, Xbest is the best organism (that is the one that resulted in the minimum value
of the fitness function), and rand(0,1) is a random number in the range [0, 1]. The parameters BF1 and BF2
are called benefit factors which represent the level of benefit between the organisms as the organisms may
fully or partially benefit from such a relationship. They are randomly determined as either 1 or 2. At the end
of the mutualism phase, the new organisms are accepted, and taken to the next phase, if they give better
(that is less) fitness function values than their pre-interaction fitness values.
2.2. Commensalism phase
Similar to the first phase, an organism Xj is randomly selected to interact with Xi. However, in this
phase, Xi tries to benefit from the interaction, while Xj itself is not affected at all. Then, Xi is updated using
the following expression [9]:
(3)
The term (Xbest – Xj ) reflects the benefit provided by Xj to Xi to increase its degree of adaptation
(to the highest degree represented by Xbest) such that it can better survive in the ecosystem. The new
organism Xi (new) is adopted, and taken to the third phase, if it results in a better fitness function value. If not,
then Xi remains the same, which means that it didn’t gain anything from such an interaction with Xj.
2.3. Parasitism phase
In this last third phase, a new vector called “Parasite Vector” is created by mutating Xi in a random
dimension using a random number. Then, an organism Xj is randomly selected to serve as a host to
the parasite vector, and the fitness function for both organisms is evaluated. If the parasite vector has a better
fitness, then Xj is removed and its position is taken over by the parasite vector. On the other hand, if Xj has
a better fitness value, then it will be immune from the parasite vector, and will not be affected by its
existence. In this case, the parasite vector is simply removed from the ecosystem. A pseudo-code and
a detailed description of the SOS algorithm can be found in [9].
3. PROBLEM FORMULATION
One of the most important issues in the CMOS inverters is the symmetric switching speed.
In CMOS inverters circuits, the switching speed is affected by the output resistance of the high state of
the P-MOSFET, the capacitive load, the length and width of the PMOS and the NMOS channels and on both
the input voltage Vin and the power supply. The switching speed can be improved by decreasing the output
resistance and this can be done by increasing the power supply. However, the larger power supply will result
in increased power dissipation. In this paper, the CMOS inverter is optimized to operate at 2.5 V, and
the switching operation is analyzed to determine the rise and fall times (tr and tf ) and propagation delays (tpHL
and tpLH ). The PSPICE schematic diagram of the CMOS inverter is shown in Figure 1, while the input and
the output waveforms are shown in Figure 2.
As well known, the fall time (tf ), is the time required for the output voltage to drop from V90% of
the output voltage level down to V10%, and the rise time (tr ) is defined as the time required for the output
voltage to rise from V10% to V90%. They can be computed as follows [3-8]:
*
( )
+ (4)
[
( ( ))
] (5)
On the other hand, the high-to-low propagation delay time (tpHL ) is defined as the time delay
between the V50% transition of the rising input voltage and the V50% transition of the falling output voltage,
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and can be computed as [3-8]:
*
( )
+ (6)
Lastly, the low-to-high propagation delay time (tpLH ) is defined as the time delay between the V50%
transition of the falling input voltage and the V50% transition of the rising output voltage, and can be
computed as [3-8]:
[
( ( | |))
] (7)
Figure 1. CMOS inverter circuit (as drawn in PSIPICE)
Figure 2. Input and output voltage waveforms of the CMOS inverter
The cost (fitness) function to be minimized can be written as [3-8]:
(8)
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where
|( ( ( ) )) | |( ) |
(9)
Subject to the following constraints:
( )
( )
Thus, the objective of the optimization is to get, as much as possible, a symmetric response of
the inverter, such that the fall and rise times are equal, and the propagation delays are equal. There are three
variables in the optimization problem, namely, the load capacitance CL, and the ratio between the widths and
the lengths of the channels, and . Thus, the dimension of the optimization problem is 3.
The minimum and maximum values of the above constraints are shown in Table 1, where similar to [3-8],
8 different design sets are considered in this paper. Moreover, similar to [1-8], TSMC 0.25 micron fabrication
technology is considered, where: VDD= 2.5 V, Vtn = 0.3655 V, Vtp = 0.5466 V, = 243.6 A/V2
, and
= 51.6 A/V2
.
Table 1. Constraints values for the 8 design sets [3-8]
Design set no. CL (pF) (W/L)n (W/L)p tf (ns) tr (ns) tpHL (ns) tpLH (ns)
1 0.2–4 1.1–6.1 2.8–19.3 1.1–13 1.1–13 0.5–10 0.5–10
2 0.1–5.1 1.6–7.1 1.8–18 1.1–15 1.1–15 0.5–8 0.5–8
3 0.47–2 1.4–6.7 3.2–38 0.5–12 0.5–12 0.2–9 0.2–9
4 0.1-1.1 1.2–7 1.5-17.5 0.5–5 0.5–5 0.2–4 0.2–4
5 0.2–14 1.9–5.0 2.7–17 0.7–6 0.7–6 0.4–5 0.4–5
6 0.3–3.6 1.3–3.5 3.5–16.2 0.25–7 0.25–7 0.3–4.5 0.3–4.5
7 0.2–4.9 1.1–5.8 2.2–25.3 0.5–6.6 0.5–6.6 0.2–7.7 0.2–7.7
8 0.2–3.5 0.3–7.6 1.3–39 0.3–6.6 0.3–6.6 0.1–4.4 0.1–4.4
4. NUMERICAL RESULTS
For each design set in Table 1, SOS code with population size=10 and maximum number of
iterations=500 is run for 50 independent times. Each run took around 8 seconds on an HP ProBook 6550b
Laptop with “Intel Core i7 CPU M 620 @ 2.67 GHz” and “4 GB RAM”. Table 2 shows the best results
obtained using SOS for each design set, where it can be seen that a symmetric output waveform is obtained
with equal fall and rise times and almost equal propagation delays. In Table 3, SOS results are compared with
the best-known results found in the literature. Namely, SOS results are compared with those obtained using
particle swarm optimization with constriction factor and inertia weight approach (PSOCFIWA) [3],
differential evolution (DE) [4], craziness based particle swarm optimization (CRPSO) [5], hybrid harmony
search with differential evolution (HS-DE) [6], and particle swarm optimization with aging leader and
challenger (ALC-PSO) [7]. It should be mentioned that we took the values of the input parameters (CL, ,
) from the tables in [3-7] and computed the corresponding CF values ourselves, as it is believed that
the reported CF values in [3-7] are not right. Even though SOS does not get the least error in all design sets,
it does compete very well with other well-developed methods. It should be also mentioned that, unlike other
methods, SOS is free of tuning parameters; one has to set the values of the population size and the maximum
number of iterations only. Moreover, the basic SOS algorithm is used here without any modifications,
in contrary to [3, 5, 7] where PSO was modified to overcome some of its inherent drawbacks. Table 4 shows
some statistical measures of the SOS results. It can be clearly seen that the SOS is a robust optimization
method as it obtains a very small value for the standard deviation (compared, for example, with the one
reported in [5] using CRPSO). Figure 3 shows the convergence curve for the design set number 8 which
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176
obtains the least value for CF. Very good convergence is reached within only 100 iterations. Figure 4 shows
box-and-whisker plot for the same design set. In this figure, median (middle of dataset) is represented by
the red line, i.e., 50% of the data are larger than this value. Upper and lower ends of the box represent
the 75th and 25th percentiles, i.e., 25% of the data are larger than the upper end, while 25% of the data are
less than the lower end. The difference between the upper and lower ends of the box (i.e., the length of
the box) is known as the “interquartile range (IQR)”. The whiskers are lines extending from the ends of
the box to show the extent of the rest of the data. The end of the upper whisker indicates the largest value in
the dataset (excluding upper outliers), while the end of the lower whisker indicates the smallest value in
the dataset (excluding lower outliers). Outliers are data with values beyond the ends of the whiskers,
and are indicated with red “+” sign. An outlier is any value that lies more than one and a half times the length
of the box from either end of the box.
Table 2. SOS results
Design set no. CL (pF) (W/L)n (W/L)p tf (ns) tr (ns) tpHL (ns) tpLH (ns) CF (ps)
1 0.7428 3.49783 18.67556 1.1785 1.1785 0.5 0.51835 18.35
2 0.71593 3.3713 18 1.1785 1.1785 0.5 0.51835 18.35
3 0.60366 6.7 35.77255 0.5 0.5 0.21213 0.21992 7.79
4 0.10812 1.2 6.40702 0.5 0.5 0.21213 0.21993 7.79
5 0.32279 1.9 10.14445 0.9428 0.9428 0.4 0.41469 14.68
6 0.3866 3.03417 16.2 0.7071 0.7071 0.3 0.31101 11.01
7 0.42687 4.73782 25.29611 0.5 0.5 0.21213 0.21992 7.79
8 0.39487 7.30448 39 0.3 0.3 0.12728 0.13195 4.67
Table 3. Comparison of SOS-obtained CF values (in ps) with other methods in the literature.
The least CF value is in boldface
Design set no. SOS CRPSO
[5]
RGA
[5]
HS-DE
[6]
PSO-CFIWA
[3]
DE
[4]
ALC-PSO
[7]
1 18.35 31.84 65.77 17.74 18.26 37.26 17.83
2 18.35 35.06 75.09 17.92 18.17 39.5 17.85
3 7.79 15.37 77.54 7.81 7.79 16.01 7.8
4 7.79 15.12 36.83 7.79 7.79 12.81 13.68
5 14.68 30.04 69.28 14.19 14.46 42.13 14.19
6 11.01 20.59 70.92 22.95 11.53 23.58 10.96
7 7.79 8.84 69.25 7.81 7.9 15.97 7.79
8 4.67 7.91 68.78 4.67 4.67 9.35 4.67
Table 4. Statistics of SOS CF values out of 50 runs for each set
Set 1 Set 2 Set 3 Set 4 Set 5 Set 6 Set 7 Set 8
Set 8
(CRPSO) [5]
Minimum (ps) 18.3538 18.3538 7.78695 7.78695 14.683 11.01227 7.78695 4.67217 5.2594
Maximum (ps) 18.537 18.8628 7.847 7.88833 14.8548 11.04983 7.97586 4.76056 15.3432
Mean (ps) 18.3853 18.3815 7.7943 7.7983 14.6958 11.01903 7.81208 4.68196 10.0572
Standard deviation (x 10-14
) (s) 4.1548 7.9977 1.3174 1.9595 2.66413 0.89952 3.684 1.53946 108.15
The 0.25 micron CMOS inverter circuit is simulated using the new version of PSPICE (V 17.7).
The results of eight different design set are obtained and shown in Table 5, considering the same SOS
optimized values as shown in Table 2 of the important parameters, output load capacitor and the geometry of
the transistor, as inputs for PSPICE simulation. Figures 5 and 6 shows the timing diagrams for the rise and
fall times and the propagation delay times, respectively, for design set number 8 using PSPICE simulation.
A reasonable similarity between the SOS results in Table 2 and PSPICE results in Table 5 for the rise and fall
times can be observed. However, larger group delays are predicted using PSPICE. This is due to the fact that
more complex expressions are used in PSPICE to evaluate the rise and fall times and the propagation delays,
while simple approximate expressions are used here assuming long channel transistors and an ideal step input
voltage. Similar observation has been noted in [3-7]. Clearly, these approximate expressions underestimate
the propagation delays, and thus, we are now exploring the use of more complex expressions, like
the improved ones very recently proposed in [25].
It is worth mentioning that PSPICE results are obtained using V17.7 with the following settings:
a. Simulation profile:
Analysis type = Transient
TSTOP: 8 seconds
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Start saving data after: 0 seconds
Maximum step size: 1ms
SKIPBP: true
All other simulation parameters are left to their default values (RELTOL, VNTOL, ABSTOL, CHGTOL,
GMIN, ITL1, ITL2, ITL4, TNOM, THREADS, Speed level, SKIPBP).
b. CMOS technology used: MbreakP and MbreakN to simulate the 0.25 μm TSMC fabrication technology
with the parameters mentioned in the paper (μn Cox, Vtn, Vtp, (W/L)n, (W/L)p).
c. Input source: (VPULSE) source with the following characteristics:
V1 = 0; V2 = 2.5 = VDD; TD = 0; TR = 1 n; TF = 1 n; PW = 1; PER = 2
Figure 3. Convergence curve for the SOS results for
the design set number 8
Figure 4. Box-and-whisker plot for the SOS results
for design set number 8
Table 5. PSPICE results using Vin “High” = VDD
Design set no. CL (pF) (W/L)n (W/L)p
tf
(ns)
tr
(ns)
tpHL
(ns)
tpLH
(ns)
CF (ns)
1 0.7428 3.49783 18.67556 1.35524 0.96916786 1.1966 1.1166 0.46607
2 0.71593 3.3713 18 1.35524 0.96916741 1.0971 1.1627 0.45167
3 0.60366 6.7 35.77255 0.76703569 0.64575389 0.887348 0.602467 0.40616
4 0.10812 1.2 6.40702 0.76704227 0.64575767 0.914830 0.627832 0.40828
5 0.32279 1.9 10.14445 1.15790 0.83511820 1.0645 0.715316 0.67197
6 0.3866 3.03417 16.2 1.00256 0.70468631 0.961739 0.673224 0.58639
7 0.42687 4.73782 25.29611 0.7670353 0.64575389 0.878689 0.566099 0.43387
8 0.39487 7.30448 39 0.679782 0.668853 0.795644 0.512026 0.29455
Figure 5. Fall time and rise time timing diagrams obtained using PSPICE for design set 8
0 50 100 150 200 250 300 350 400 450 500
-114
-113
-112
-111
-110
-109
-108
-107
-106
-105
-104
X: 97
Y: -113.3
iteration
fitnessfunctionvalue,J
4.67
4.68
4.69
4.7
4.71
4.72
4.73
4.74
4.75
4.76
Case 3, set 8
CFvalue(ps)
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5. CONCLUSION
In this paper, SOS algorithm was used in the optimal design of a CMOS inverter with symmetric
switching characteristics. SOS results were compared with those obtained using other evolutionary
optimization methods available in the literature. The comparison showed that SOS is a robust, simple, and
fast converging method. For all 8 design sets, SOS method performed very well, which clearly shows
the effectiveness of the SOS method. As mentioned before, compared to other methods, SOS has
the advantage of being free of tuning parameters; one just needs to set the values of the population size and
the maximum number of iterations.
ACKNOWLEDGEMENTS
This research was part of the sabbatical leave of the first author which was supported by Jordan
University of Science and Technology, Irbid, Jordan. The authors acknowledge the help of late Dr. B. El-Asir
with the PSPICE simulations.
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