To mine out relevant facts at the time of need from web has been a tenuous task. Research on diverse fields are fine tuning methodologies toward these goals that extracts the best of information relevant to the users search query. In the proposed methodology discussed in this paper find ways to ease the search complexity tackling the severe issues hindering the performance of traditional approaches in use. The proposed methodology find effective means to find all possible semantic relatable frequent sets with FP Growth algorithm. The outcome of which is the further source of fuel for Bio inspired Fuzzy PSO to find the optimal attractive points for the web documents to get clustered meeting the requirement of the search query without losing the relevance. On the whole the proposed system optimizes the objective function of minimizing the intra cluster differences and maximizes the inter cluster distances along with retention of all possible relationships with the search context intact. The major contribution being the system finds all possible combinations matching the user search transaction and thereby making the system more meaningful. These relatable sets form the set of particles for Fuzzy Clustering as well as PSO and thus being unbiased and maintains a innate behaviour for any number of new additions to follow the herd behaviour’s evaluations reveals the proposed methodology fares well as an optimized and effective enhancements over the conventional approaches
Ml srhwt-machine-learning-based-superlative-rapid-haar-wavelet-transformation...Jumlesha Shaik
Abstract: In this paper a digital image coding technique called ML-SRHWT (Machine Learning based image coding by Superlative Rapid HAAR Wavelet Transform) has been introduced. Compression of digital image is done using the model Superlative Rapid HAAR Wavelet Transform (SRHWT). The Least Square Support vector Machine regression predicts hyper coefficients obtained by using QPSO model. The mathematical models are discussed in brief in this paper are SRHWT, which results in good performance and reduces the complexity compared to FHAAR and EQPSO by replacing the least good particle with the new best obtained particle in QPSO. On comparing the ML-SRHWT with JPEG and JPEG2000 standards, the former is considered to be the better.
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.
Advances in the Solution of Navier-Stokes Eqs. in GPGPU Hardware. Modelling F...Storti Mario
In this article we compare the results obtained with an implementation of the Finite Volume for structured meshes on GPGPUs with experimental results and also with a Finite Element code with boundary fitted strategy. The example is a fully submerged spherical buoy immersed in a cubic water recipient. The recipient undergoes an harmonic linear motion imposed with a shake table. The experiment is recorded with a high speed camera and the displacement of the buoy if obtained from the video with a MoCap (Motion Capture) algorithm. The amplitude and phase of the resulting motion allows to determine indirectly the added mass and drag of the sphere.
Acceleration of the Longwave Rapid Radiative Transfer Module using GPGPUMahesh Khadatare
This poster presents Weather Research and Forecast (WRF)
model is a next-generation mesoscale numerical weather
prediction system designed to serve both operational forecasting
and atmospheric research communities. WRF offers multiple
physics options, one of which is the Long-Wave Rapid Radiative
Transfer Model (RRTM). Even with the advent of large-scale
parallelism in weather models, much of the performance increase
has came from increasing processor speed rather than increased
parallelism. We present an alternative method of scaling model
performance by exploiting emerging architectures like GPGPU
using the fine-grain parallelism. We claim to get much more than
23.71x, performance gain by using asynchronous data transfer,
use of texture memory and the techniques like loop unrolling.
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
International Journal of Managing Information Technology (IJMIT)IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph, the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network. SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed. In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient
Ml srhwt-machine-learning-based-superlative-rapid-haar-wavelet-transformation...Jumlesha Shaik
Abstract: In this paper a digital image coding technique called ML-SRHWT (Machine Learning based image coding by Superlative Rapid HAAR Wavelet Transform) has been introduced. Compression of digital image is done using the model Superlative Rapid HAAR Wavelet Transform (SRHWT). The Least Square Support vector Machine regression predicts hyper coefficients obtained by using QPSO model. The mathematical models are discussed in brief in this paper are SRHWT, which results in good performance and reduces the complexity compared to FHAAR and EQPSO by replacing the least good particle with the new best obtained particle in QPSO. On comparing the ML-SRHWT with JPEG and JPEG2000 standards, the former is considered to be the better.
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.
Advances in the Solution of Navier-Stokes Eqs. in GPGPU Hardware. Modelling F...Storti Mario
In this article we compare the results obtained with an implementation of the Finite Volume for structured meshes on GPGPUs with experimental results and also with a Finite Element code with boundary fitted strategy. The example is a fully submerged spherical buoy immersed in a cubic water recipient. The recipient undergoes an harmonic linear motion imposed with a shake table. The experiment is recorded with a high speed camera and the displacement of the buoy if obtained from the video with a MoCap (Motion Capture) algorithm. The amplitude and phase of the resulting motion allows to determine indirectly the added mass and drag of the sphere.
Acceleration of the Longwave Rapid Radiative Transfer Module using GPGPUMahesh Khadatare
This poster presents Weather Research and Forecast (WRF)
model is a next-generation mesoscale numerical weather
prediction system designed to serve both operational forecasting
and atmospheric research communities. WRF offers multiple
physics options, one of which is the Long-Wave Rapid Radiative
Transfer Model (RRTM). Even with the advent of large-scale
parallelism in weather models, much of the performance increase
has came from increasing processor speed rather than increased
parallelism. We present an alternative method of scaling model
performance by exploiting emerging architectures like GPGPU
using the fine-grain parallelism. We claim to get much more than
23.71x, performance gain by using asynchronous data transfer,
use of texture memory and the techniques like loop unrolling.
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
International Journal of Managing Information Technology (IJMIT)IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph, the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network. SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed. In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
Impact of Auto-tuning of Kernel Loop Transformation by using ppOpen-ATTakahiro Katagiri
SPNS2013, December 5th -6th, 2013, Conference Room, 3F, Bldg.1, Earthquake Research Institute (ERI), The University of Tokyo, December 6th, 2013, ppOpen-HPC and Automatic Tuning (Chair: Hideyuki Jitsumoto), 1330-1400
The Shortest Path Tour Problem is an extension to the normal Shortest Path Problem and appeared in the scientific literature in Bertsekas's dynamic programming and optimal control book in 2005, for the first time. This paper gives a description of the problem, two algorithms to solve it. Results to the numeric experimentation are given in terms of graphs. Finally, conclusion and discussions are made.
Molecular dynamics (MD) is a very useful tool to understand various phenomena in atomistic detail. In MD, we can overcome the size- and time-scale problems by efficient parallelization. In this lecture, I’ll explain various parallelization methods of MD with some examples of GENESIS MD software optimization on Fugaku.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
COMPUTATIONAL PERFORMANCE OF QUANTUM PHASE ESTIMATION ALGORITHMcsitconf
A quantum computation problem is discussed in this paper. Many new features that make
quantum computation superior to classical computation can be attributed to quantum coherence
effect, which depends on the phase of quantum coherent state. Quantum Fourier transform
algorithm, the most commonly used algorithm, is introduced. And one of its most important
applications, phase estimation of quantum state based on quantum Fourier transform, is
presented in details. The flow of phase estimation algorithm and the quantum circuit model are
shown. And the error of the output phase value, as well as the probability of measurement, is
analysed. The probability distribution of the measuring result of phase value is presented and
the computational efficiency is discussed.
COMPUTATIONAL PERFORMANCE OF QUANTUM PHASE ESTIMATION ALGORITHMcscpconf
A quantum computation problem is discussed in this paper. Many new features that make quantum computation superior to classical computation can be attributed to quantum coherence
effect, which depends on the phase of quantum coherent state. Quantum Fourier transform algorithm, the most commonly used algorithm, is introduced. And one of its most important
applications, phase estimation of quantum state based on quantum Fourier transform, is presented in details. The flow of phase estimation algorithm and the quantum circuit model are
shown. And the error of the output phase value, as well as the probability of measurement, is analysed. The probability distribution of the measuring result of phase value is presented and the computational efficiency is discussed.
Yuki Oyama - Incorporating context-dependent energy into the pedestrian dynam...Yuki Oyama
Oyama, Y., Hato, E. (2015) Incorporating context-dependent energy into the pedestrian dynamic scheduling model with GPS data. The 14th International Conference on Travel Behaviour research (IATBR), Windsor, England.
Digital Signal Processing[ECEG-3171]-Ch1_L06Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Digital Signal Processing[ECEG-3171]-Ch1_L05Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
I studied in Indian Institute of Technology, Kharagpur, India. I did my B.Texh and M.Tech in the department of Electronics and Electrical Communication Engineering. I was student of 2018 batch. After that, I joined Schneider Electric Systems India Private limited Company as Software design Engineer. Currently I am designated as Senior Firmware Engineer in the same company. I have work experience of 4+ years. The uploaded ppt is my MTP Thesis. It is about "temperature aware application mapping on to mesh based network on chip using Genetic Algorithm".
SLIDING WINDOW SUM ALGORITHMS FOR DEEP NEURAL NETWORKSIJCI JOURNAL
Sliding window sums are widely used for string indexing, hashing and time series analysis. We have
developed a family of the generic vectorized sliding sum algorithms that provide speedup of O(P/w) for
window size w and number of processors P. For a sum with a commutative operator the speedup is
improved to O(P/log(w)). Even more important, our algorithms exhibit efficient memory access patterns. In
this paper we study the application of sliding sum algorithms to the training and inference of Deep Neural
Networks. We demonstrate how both pooling and convolution primitives could be expressed as sliding
sums and evaluated by the compute kernels with a shared structure. We show that the sliding sum
convolution kernels are more efficient than the commonly used GEMM kernels on CPUs and could even
outperform their GPU counterparts.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
An improved spfa algorithm for single source shortest path problem using forw...IJMIT JOURNAL
We present an improved SPFA algorithm for the single source shortest path problem. For a random graph,
the empirical average time complexity is O(|E|), where |E| is the number of edges of the input network.
SPFA maintains a queue of candidate vertices and add a vertex to the queue only if that vertex is relaxed.
In the improved SPFA, MinPoP principle is employed to improve the quality of the queue. We theoretically
analyse the advantage of this new algorithm and experimentally demonstrate that the algorithm is efficient.
Impact of Auto-tuning of Kernel Loop Transformation by using ppOpen-ATTakahiro Katagiri
SPNS2013, December 5th -6th, 2013, Conference Room, 3F, Bldg.1, Earthquake Research Institute (ERI), The University of Tokyo, December 6th, 2013, ppOpen-HPC and Automatic Tuning (Chair: Hideyuki Jitsumoto), 1330-1400
The Shortest Path Tour Problem is an extension to the normal Shortest Path Problem and appeared in the scientific literature in Bertsekas's dynamic programming and optimal control book in 2005, for the first time. This paper gives a description of the problem, two algorithms to solve it. Results to the numeric experimentation are given in terms of graphs. Finally, conclusion and discussions are made.
Molecular dynamics (MD) is a very useful tool to understand various phenomena in atomistic detail. In MD, we can overcome the size- and time-scale problems by efficient parallelization. In this lecture, I’ll explain various parallelization methods of MD with some examples of GENESIS MD software optimization on Fugaku.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
COMPUTATIONAL PERFORMANCE OF QUANTUM PHASE ESTIMATION ALGORITHMcsitconf
A quantum computation problem is discussed in this paper. Many new features that make
quantum computation superior to classical computation can be attributed to quantum coherence
effect, which depends on the phase of quantum coherent state. Quantum Fourier transform
algorithm, the most commonly used algorithm, is introduced. And one of its most important
applications, phase estimation of quantum state based on quantum Fourier transform, is
presented in details. The flow of phase estimation algorithm and the quantum circuit model are
shown. And the error of the output phase value, as well as the probability of measurement, is
analysed. The probability distribution of the measuring result of phase value is presented and
the computational efficiency is discussed.
COMPUTATIONAL PERFORMANCE OF QUANTUM PHASE ESTIMATION ALGORITHMcscpconf
A quantum computation problem is discussed in this paper. Many new features that make quantum computation superior to classical computation can be attributed to quantum coherence
effect, which depends on the phase of quantum coherent state. Quantum Fourier transform algorithm, the most commonly used algorithm, is introduced. And one of its most important
applications, phase estimation of quantum state based on quantum Fourier transform, is presented in details. The flow of phase estimation algorithm and the quantum circuit model are
shown. And the error of the output phase value, as well as the probability of measurement, is analysed. The probability distribution of the measuring result of phase value is presented and the computational efficiency is discussed.
Yuki Oyama - Incorporating context-dependent energy into the pedestrian dynam...Yuki Oyama
Oyama, Y., Hato, E. (2015) Incorporating context-dependent energy into the pedestrian dynamic scheduling model with GPS data. The 14th International Conference on Travel Behaviour research (IATBR), Windsor, England.
Digital Signal Processing[ECEG-3171]-Ch1_L06Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Digital Signal Processing[ECEG-3171]-Ch1_L05Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
I studied in Indian Institute of Technology, Kharagpur, India. I did my B.Texh and M.Tech in the department of Electronics and Electrical Communication Engineering. I was student of 2018 batch. After that, I joined Schneider Electric Systems India Private limited Company as Software design Engineer. Currently I am designated as Senior Firmware Engineer in the same company. I have work experience of 4+ years. The uploaded ppt is my MTP Thesis. It is about "temperature aware application mapping on to mesh based network on chip using Genetic Algorithm".
SLIDING WINDOW SUM ALGORITHMS FOR DEEP NEURAL NETWORKSIJCI JOURNAL
Sliding window sums are widely used for string indexing, hashing and time series analysis. We have
developed a family of the generic vectorized sliding sum algorithms that provide speedup of O(P/w) for
window size w and number of processors P. For a sum with a commutative operator the speedup is
improved to O(P/log(w)). Even more important, our algorithms exhibit efficient memory access patterns. In
this paper we study the application of sliding sum algorithms to the training and inference of Deep Neural
Networks. We demonstrate how both pooling and convolution primitives could be expressed as sliding
sums and evaluated by the compute kernels with a shared structure. We show that the sliding sum
convolution kernels are more efficient than the commonly used GEMM kernels on CPUs and could even
outperform their GPU counterparts.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
USING LEARNING AUTOMATA AND GENETIC ALGORITHMS TO IMPROVE THE QUALITY OF SERV...IJCSEA Journal
A hybrid learning automata–genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP–Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature..
Multi-core GPU – Fast parallel SAR image generationMahesh Khadatare
This poster presents a design for parallel processing of synthetic
aperture radar (SAR) data using multi-core Graphics Processing
Units (GP-GPUs). In this design a two-dimensional image is
reconstructed from received echo pulses and their corresponding
response values. Then the comparative performance of proposed
parallel algorithm implementation is tabulated using different set of
nvidia GPUs i.e. GT, Tesla and Fermi. Analyzing experimentation
results on image of various size 1,024 x 1,024 pixels to 4,096 x
4,096 pixels led us to the conclusion that that nvidia Fermi S2050 have
maximum performance throughput .
α Nearness ant colony system with adaptive strategies for the traveling sales...ijfcstjournal
On account of ant colony algorithm easy to fall into local optimum, this paper presents an improved ant
colony optimization called α-AACS and reports its performance. At first, we provide an concise description
of the original ant colony system(ACS) and introduce α-nearness based on the minimum 1-tree for ACS’s
disadvantage, which better reflects the chances of a given link being a member of an optimal tour. Then, we
improve α-nearness by computing a lower bound and propose other adaptations for ACS. Finally, we
conduct a fair competition between our algorithm and others. The results clearly show that α-AACS has a
better global searching ability in finding the best solutions, which indicates that α-AACS is an effective
approach for solving the traveling salesman problem.
FPGA Implementation of A New Chien Search Block for Reed-Solomon Codes RS (25...IJERA Editor
The Reed-Solomon codes RS are widely used in communication systems, in particular forming part of the specification for the ETSI digital terrestrial television standard. In this paper a simple algorithm for error detection in the Chien Search block is proposed. This algorithm is based on a simple factorization of the error locator polynomial, which allows reducing the number of components required to implement the proposed algorithm on FPGA board. Consequently, it reduces the power consumption with a percentage which can reach 50 % compared to the basic RS decoder. First, we developed the design of Chien Search Block Second, we generated and simulated the hardware description language source code using Quartus software tools,finally we implemented the proposed algorithm of Chien search block for Reed-Solomon codesRS (255, 239) on FPGA board to show both the reduced hardware resources and low complexity compared to the basic algorithm.
DESIGN OF QUATERNARY LOGICAL CIRCUIT USING VOLTAGE AND CURRENT MODE LOGICVLSICS Design
In VLSI technology, designers main concentration were on area required and on performance of the
device. In VLSI design power consumption is one of the major concerns due to continuous increase in chip
density and decline in size of CMOS circuits and frequency at which circuits are operating. By considering
these parameter logical circuits are designed using quaternary voltage mode logic and quaternary current
mode logic. Power consumption required for quaternary voltage mode logic is 51.78 % less as compared
to binary . Area in terms of number of transistor required for quaternary voltage mode logic is 3 times
more as compared to binary. As quaternary voltage mode circuit required large area as compared to
quaternary current mode circuit but power consumption required in quaternary voltage mode circuit is less
than that required in quaternary current mode circuit .
Investigation of repeated blasts at Aitik mine using waveform cross correlationIvan Kitov
We present results of signal detection from repeated events at the Aitik and Kiruna mines in Sweden as based on waveform cross correlation. Several advanced methods based on tensor Singular Value Decomposition is applied to waveforms measured at seismic array ARCES, which consists of three-component sensors.
RADIAL BASIS FUNCTION PROCESS NEURAL NETWORK TRAINING BASED ON GENERALIZED FR...cseij
For learning problem of Radial Basis Function Process Neural Network (RBF-PNN), an optimization
training method based on GA combined with SA is proposed in this paper. Through building generalized
Fréchet distance to measure similarity between time-varying function samples, the learning problem of
radial basis centre functions and connection weights is converted into the training on corresponding
discrete sequence coefficients. Network training objective function is constructed according to the least
square error criterion, and global optimization solving of network parameters is implemented in feasible
solution space by use of global optimization feature of GA and probabilistic jumping property of SA . The
experiment results illustrate that the training algorithm improves the network training efficiency and
stability.
Similar to Automated Information Retrieval Model Using FP Growth Based Fuzzy Particle Swarm Optimization (20)
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
instrumental not just to the success of SMEs but, through their productivity and innovative contribution to
the economy, has even made a remarkable contribution to the economic growth of the United States. To
this end, the study focuses on how cloud computing technology has impacted economic growth through
SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
application of MIMO technology within WSNs. This approach operates effectively, especially in
challenging and resource-constrained environments. By facilitating collaboration among sensor nodes,
Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
industrial automation, and healthcare applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking
Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This
gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
connected wirelessly to easily track available locations.
Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
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practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed
machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
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summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
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become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption
in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a
cloud-based platform to host their services and data. Like many complex systems, cloud systems are
susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this
paper, we explore the security of key components in the EV charging infrastructure, including the mobile
application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack
between an EV app and its cloud services. Our results showed that it is possible to launch attacks against
the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and
operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions
and future research directions.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
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Automated Information Retrieval Model Using FP Growth Based Fuzzy Particle Swarm Optimization
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 1, February 2015
DOI:10.5121/ijcsit.2015.7106 59
IMPROVING INITIAL GENERATIONS IN PSO
ALGORITHM FOR TRANSPORTATION NETWORK
DESIGN PROBLEM
Navid Afkar and Abbas Babazadeh
Department of Civil Engineering, University of Tehran, Tehran, Iran
ABSTRACT
Transportation Network Design Problem (TNDP) aims to select the best project sets among a number of
new projects. Recently, metaheuristic methods are applied to solve TNDP in the sense of finding better
solutions sooner. PSO as a metaheuristic method is based on stochastic optimization and is a parallel
revolutionary computation technique. The PSO system initializes with a number of random solutions and
seeks for optimal solution by improving generations. This paper studies the behavior of PSO on account of
improving initial generation and fitness value domain to find better solutions in comparison with previous
attempts.
KEYWORDS
Transportation; Network Design; optimization; Particle Swarm; roulette cycle; initial value
1. INTRODUCTION
In transportation planning, Transportation Network Design Problem (TNDP) is a substantial area
in which specific objectives are minimized through selection among a given set of projects under
constraints (1). Solving TNDP requires too much time. Various approaches have been taken to
solve TNDP (1,2,3). One of the typical methods to solve TNDP are Meta-Heuristic algorithms
(such as Genetic Algorithm). Particle Swarm optimization (PSO) is a Meta-Heuristic algorithm
that has shown good performance to solve TNDP.
In this paper, particle swarm optimization (PSO) algorithm developed with some variations or
added methods is presented to solve the TNDP (4,5). The results of each method are compared
together and with Original PSO method. The remainder of the paper is organized as follows. The
next section is devoted to define the TNDP mathematically. In the following sections, the PSO is
described in details, and then applied to the TNDP. After that Binary Sorting, Binary Count, and
Roulette methods are defined and used to enhance the efficiency of PSO for solving the TNDP on
the Sioux Falls network. The results are obtained by a computer program in VISUAL BASIC 6.0
on a laptop with Intel core 2 due 2.4 GHz processor. In this program, each algorithm is terminated
after a fixed number of 1000 iterations. Due to the stochastic nature of PSO, the algorithms have
been solved 50 times and the results are based on the average values of the 50 runs.
Computational results and figures are reported in the final section.
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 1, February 2015
60
2. TNDP
Let ),( AVG be a graph representing a transportation network with node set V and arc set A,
and define }:),{( srVVsrP as the set of origin-destination (OD) pairs. For each OD
pair Psr ),( , there is a nonnegative flow rate (travel demand) from r to s, denoted by rsd . In
order to simplify the presentation, suppose that G is strongly connected, that is each node j can
be reached from every other node i by following a directed path in G , and let rsK be the non-
empty set of paths from the origin r to the destination s.
Define Ā (Ā ≠ ) as the set of project arcs, and let the decision vector be y = ( ) ∈Ā with
being the binary project decision variable, taking values 0 or 1 depending on rejection or
acceptance of any project ∈ Ā . For a given vector y, define the decision network G( ) =
( , ( )) with A(y) = A∪ { ∈ Ā ∶ = 1} as the set of arcs followed by decision y, and for
each (r, s) ∈ denote by ( ) the set of paths joining r to s in G( ). For each path k ∈ ( )
let be the flow of path k from origin r to destination s. Moreover, let equals 1 if arc a
∈ ( ) lies on path k, and 0 otherwise.
Assume further that each arc a ∈ ∪ Ā has a node creasing and continuously differentiable
travel time function ( ):[0, ∞) → [0, ∞) with being the flow rate assigned to arc a. Then,
letting be the construction cost of project arc a ∈ Ā , and considering the total construction
cost being limited to the level of Budget B, the TNDP can be illustrated with upper level problem,
ULP:
[ULP] T(y) = ∑ ( ∈ ( ) )
s.t. ∑ ( ) ∈ Ā
=0 or 1 ∀a ∈ Ā
X(y) is a solution of [LLP(y)]
Where x(y) = ( ) ∈ ( ) is the user equilibrium flow in the decision network G(y), given as
the solution of the lower level (traffic assignment) problem, LLP(y), for given y:
[LLP(y)] Min ∑ ∫ ( )∈ ( )
s.t. ∑ ∈ ( ) = ∀( , ) ∈
≥ 0 ∀ ∈ ( ), ∀ ( , ) ∈
= ∑ ∑ ∈( , )∈ ∀ ∈ ( )
This is a well-known bi-level programming problem, where the [ULP] seeks a decision vector
y for minimizing the total travel time T(y) of the (assigned) traveler, and the [LLP(y)] is the traffic
assignment model which estimates the traveler flows, given the decision y (6,7,8).
3. PARTICLE SWARM OPTIMIZATION
Particle Swarm Optimization (PSO) is a meta-heuristic optimization approach which has been
widely applied to various problems (4). PSO technique that was developed by Kennedy and
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 1, February 2015
61
Eberhart is originated from the behavior of birds ’flocks in which individuals convey information
between themselves and the leader in order to seek the best direction to food (9, 10).
In a problem space, each particle has a position and a velocity and it moves in the search space
with the velocity according to its own previous best position and the group’s previous best
position. The dimension of the search space can be any positive number. Considering D as the
dimension of the search space, the ith
particle’s position and velocity are represented as
1,...,i ij j D
P p
and 1,...,i ij j D
V v
respectively. Each particle maintains its own best position so
far achieved as * *
1,...,i ij j D
P p
and the global best position so far recorded by the population as
* *
1,...,g gj j D
P p
.
During the iteration time t, the velocity of the jth
dimension of each particle i is updated by:
* *
1 1 2 2( 1) ( ) ( ( ) ( )) ( ( ) ( ))ij ij ij ij gj ijv t wv t c r p t p t c r p t p t Where w is called as the
inertia weight, 1c and 2c are constant values and 1r , 2r are random numbers in the interval 0,1 .
The current position of each particle is then defined by the sum of its current velocity and its
previous position (11,12,13).
( 1) ( ) ( 1)ij ij ijp t p t v t
In order to avoid the particles from moving out of the search space, the maximum velocity during
the iterations is restricted by maxv . As proposed by Hong Zhang, et al 2005, the maximum velocity
( maxv ) is set to maxx . This results in moving more effectively in the search space and accordingly
better algorithm performance.
4. ADAPTING THE PSO TO THE TNDP
Employing the PSO for solving TNDP needs some modifications to the algorithm given in the
previous section. First, the PSO is basically developed for continuous optimization problems(14).
This is while the TNDP is formulated as a combinatorial optimization problem in terms of
variables y denoted as A -bit binary strings. To adapt the algorithm for this combinatorial
nature, one may provide some mapping from the one-dimensional real-valued space to the A -
dimensional binary space. This is done here by transforming each real number ip to its nearest
integer in 0 , 2 1
A
, and then transforming the resulting integer in to the base-2 number
system as an A -bit binary code. To facilitate the presentation, the latter transformation is
illustrated by the function ( ) : 0 , 2 1 0 ,1 .
A A
iy p Z
The PSO must also be adapted for budget constraint embedded in the [ULP](15).
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 1, February 2015
62
5. PSO ALGORITHM
Step 1. Initialization
Select the particle swarm size n, the parameters 1c and 2c , the value of the inertia weight w, and
the maximum velocity maxv .
For i 1 to n do: initialize the decision variable ip so that Bpyc iaaAa
)( ; set
and 0iv .
Set ))(..,.),((minarg 1 ng pfpfp
. Set the iteration counter 0t .
Step 2. Updating each particle's position and velocity
For i 1 to n do: generate random numbers 1r and 2r in [0, 1]; update
)()( 2211 igiiii pprcpprcwvv
; clamp in iv between the range ],[ maxmax vv as
)|,min(|)sign( maxvvvv iii ; update iii vpp ; transform ip to its nearest integer in
12,0[ ||
A
].
Step 3. Calculating each particle's fitness value
For i 1 to n do: set )( ipyy ; if Byc aaAa
then set Mpf i )( (large fitness value);
else, solve the user equilibrium problem [LLP ( y )] to compute )(yT , and set )()( yTpf i .
Step 4. Updating local bests and global best
For i 1 to n do: update ))(),((minarg iii pfpfp
.
Update ))(..,.),(),((minarg 1 ngg pfpfpfp
.
Step 5. End criterion.
Set 1 tt . If end criterion is not met, go to Step 2. Otherwise, )(
gpyy is the best solution
found so far with the objective function value )()(
gpfyT Collect the necessary information
and stop.
6. SIOUX FALLS NETWORK
The Sioux Falls network has 24 nodes and 76 arcs, as shown in Fig. 1. The parameters of the
travel time function
4
)( aaaaa xxt for each arc a, and the OD (origin/destination)
demands are basically those given in Poorzahedy and Turnquist (1982), and LeBlanc (1975), and
are eliminated here for brevity(16).
There are 10 pairs of project arcs )10|(| A , of which 5 projects are improvement on existing arcs,
and 5 are new arcs. The construction costs of the projects 1-10 are, respectively, 625, 650, 850,
1000, 1200, 1500, 1650, 1800, 1950, and 2100 units of money (Poorzahedy and Abulghasemi
2005). Considering 10 projects, there are )1024(210
alternative networks. A complete
enumeration was used to compute the optimal solution of the TNDP for any given budget level
for checking purposes (Poorzahedy and Abulghasemi 2005; Poorzahedy and Rouhani 2007).
ii pp
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 1, February 2015
63
7. BINARY SORTING (BS)
Binary Sorting is a type of mapping the objective function value domain from
:[0 ,2 1]n
ix Z to :[0 ,2 1]n
mp Z . This results in a more effective sorting on the
function domain and puts the decision variables with the same number of projects beside each
other, therefore the PSO algorithm searches with more intelligence toward the optimum solution.
The algorithm is expressed below:
Let iB be the binary value of :[0 ,2 1]n
ix Z in base-2 number system then count the iB
number of digits and put them into iC .
m=1
for d=0 to n
for i=0 to 2n
-1
if > −1 then
if iC =d then
m ip x
1ix
m = m+1
End if
End if
Next i
Next d
8. BINARY COUNT (BC)
Binary Count is an initialization strategy which is defined for this specific problem. The idea
comes from choosing more particles which are near to the budget level. The algorithm is outlined
below.
For d=0 to n
For i=0 to 2n
-1
If Ci=d then count (d) =count (d) + 1
Next i
Next d
Assume b/c
all=int(b/c * n)+1
For i=0 to all
numb(i)=i / ∑ * n
sum numb= numb(i)+sumnumb
Next i
m=1
for i=1 to all
for d=1 to numb(i)
Select a particle that has i binary digit
Next d
Next i
Do while sum numb(i) < n
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 1, February 2015
64
Select a particle that has at most all binary digits
Loop
9. ROULETTE WHEEL SELECTION (ALGORITHM)
It is the most common selection strategy. It will assign to each individual a selection probability
that is proportional to its relative fitness. Let be the fitness of the individual in the population
P. Its probability to be selected is = / (∑ ).Suppose a pie graph where each individual is
assigned a space on the graph that is proportional to its fitness. An outer roulette wheel is placed
around the pie. The selection of μ individuals is performed by μ independent spins of the roulette
wheel. Each spin will select a single individual. Better individuals have more space, and then
more chance to be chosen. Moreover, when all individuals are equally fit, this selection strategy
does not introduce a sufficient pressure to select the best individuals. This method is applied on
the initialization step of the PSO algorithm to improve its initial generation and the combined
algorithm is named as Roulette in the following discussions.
10. COMPUTATIONAL RESULTS
In this study, the algorithms comprised of the above strategies are compared from various
perspectives. First, Average Objective Function Value (OFV) related to the initial generation of
each algorithm is shown in figure 2. According to this figure, Roulette and Roulette-BS (Binary
Sorting) generate better initial particles than the other methods.
In figure 3, BC method shows a good convergence performance concerning its decreasing
behavior despite of the relative large quantity of the initial average OFV and after 70 iterations
average OFV of BC method gets very close to average OFV of the Roulette-BS. Still, it is
obvious that the Roulette-BS method shows the best performance based on initialization and
convergence capability and its graph stands under the others in figure 3.
To perceive the effectiveness of Binary Sorting, Random and Random-BS methods are compared
with each other. The Random-BS method has more decreasing behavior than the Random method
and shows more convergence to the optimum solutions in the last iterations. So we can figure out
that the BS method makes an improvement on the PSO algorithm which is adapted to the TNDP.
Figure 4 depicts the frequency of finding the optimal solution in iterations for each method. It is
clear that each algorithm graph that stands upper than the other graphs is more powerful to find
the optimum solution. Comparison of these methods proves that the Roulette and Random
methods have less capability than the other three methods which have used binary sorting to find
the optimum solution.
The average Number of Traffic Assignment Problem Solved (NTAPS) of each method is shown
in figure 5. All of the graphs are showing decreasing behavior. This fact proves that all methods
are trying to decrease the NTAPS. As the results show, Roulette and Random graphs stand upper
than others after 50 iterations.
Figure 6 displays initialization time and PSO algorithm time usage in each method. Initialization
time of BC method is shorter than the other methods. It’s obvious that initialization time for BC
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 1, February 2015
65
method is shorter than Roulette and Roulette-BS methods, but the reason why this time is shorter
than Random method’s time is related to unsuccessful tries of generating particles with higher
budget than the problem’s budget constraint in Random method. As a result, we can figure out
that in TNDP problems with large scale of particles, the BC method shows better performance
and can find the optimum solution sooner.
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Figure 1. The Sioux Falls Network
Figure 2. Average Objective Function Value for first 10 Particles
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Figure 3. Average Objective Function Value
Figure 4. Frequency of Finding
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Figure 5. Average Number of Traffic Assignment Problem Solved
Figure 6. Initialization time and PSO algorithm time