The design of public transportation networks presupposes solving optimization problems,
involving various parameters such as the proper mathematical description of networks, the
algorithmic approach to apply, and also the consideration of real-world, practical
characteristics such as the types of vehicles in the network, the frequencies of routes, demand,
possible limitations of route capacities, travel decisions made by passengers, the environmental
footprint of the system, the available bus technologies, besides others. The current paper
presents the progress of the work that aims to study the design of a municipal public
transportation system that employs middleware technologies and geographic information
services in order to produce practical, realistic results. The system employs novel optimization
approaches such as the particle swarm algorithms and also considers various environmental
parameters such as the use of electric vehicles and the emissions of conventional ones.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BA...civejjour
Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.
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.
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, 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
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BA...civejjour
Land use and transportation planning are inter-dependent, as well as being important factors in forecasting urban development. In recent years, predicting traffic based on land use, along with several other variables, has become a worthwhile area of study. In this paper, it is proposed that Deep Neural Network Regression (DNN-Regression) and Recurrent Neural Network (DNN-RNN) methods could be used to predict traffic. These methods used three key variables: land use, demographic and temporal data. The proposed methods were evaluated with other methods, using datasets collected from the City of Calgary, Canada. The proposed DNN-Regression focused on demographic and land use variables for traffic prediction. The study also predicted traffic temporally in the same geographical area by using DNN-RNN. The DNN-RNN used long short-term memory to predict traffic. Comparative experiments revealed that the proposed DNN-Regression and DNN-RNN models outperformed other methods.
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.
International Journal of Business and Management Invention (IJBMI)inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, 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
Improved algorithm for road region segmentation based on sequential monte car...csandit
In recent years, many researchers and car makers put a lot of intensive effort into development
of autonomous driving systems. Since visual information is the main modality used by human
driver, a camera mounted on moving platform is very important kind of sensor, and various
computer vision algorithms to handle vehicle surrounding situation are under intensive
research. Our final goal is to develop a vision based lane detection system with ability to
handle various types of road shapes, working on both structured and unstructured roads, ideally
under presence of shadows. This paper presents a modified road region segmentation algorithm
based on sequential Monte-Carlo estimation. Detailed description of the algorithm is given,
and evaluation results show that the proposed algorithm outperforms the segmentation
algorithm developed as a part of our previous work, as well as an conventional algorithm based
on colour histogram.
This work considers the multi-objective optimization problem constrained by a system of bipolar fuzzy relational equations with max-product composition. An integer optimization based technique for order of preference by similarity to the ideal solution is proposed for solving such a problem. Some critical features associated with the feasible domain and optimal solutions of the bipolar max-Tp equation constrained optimization problem are studied. An illustrative example verifying the idea of this paper is included. This
is the first attempt to study the bipolar max-T equation constrained multi-objective optimization problems
from an integer programming viewpoint.
Bit Error Rate Analysis in Multicast Multiple Input Multiple Output Systemsrahulmonikasharma
At the present time whole information and communication technology industry contributes to the global carbon emission. With the aim of reducing the carbon footprint and the operating cost of wireless networks, overall energy reduction is required in the region of two to three orders of magnitude. Meanwhile, significant increase of the network spectrum efficiency is needed to cope with the exponentially increasing traffic loads. Due to this factors spatial modulation (SM) has recently established itself as promising transmission concept which belongs to single-radio frequency large scale multiple input multiple output (MIMO) wireless system. Spatial modulation MIMO takes advantage of whole antenna array at the transmitter, while using limited number of radio frequency chains. The multiple input multiple output multiplies capacity by transmitting different signals over multiple antennas and orthogonal frequency division multiplexing (OFDM), which divides a radio channel into many closely spaced sub channels to provide more reliable communication at high speeds. The system calculate the bit error rate (BER) for multicast multiple input multiple output system with the spatial modulation (SM) and study the effect of signal to noise ratio on bit error rate. MATLAB software is use to simulate system. The simulation results show that bit error rate decreases as signal to noise ratio increases. System reaches zero bit error rate for the value of signal to noise ratio greater than 18dB. System has provided less bit error rate for large signal to noise ratio which improves system performance.
Centrality Prediction in Mobile Social NetworksIJERA Editor
By analyzing evolving centrality roles using time dependent graphs, researchers may predict future centrality values. This may prove invaluable in designing efficient routing and energy saving strategies and have profound implications on evolving social behavior in dynamic social networks. In this paper, we propose a new method to predict centrality values of nodes in a dynamic environment. The proposed method is based on calculating the correlation between current and past measure of centrality for each corresponding node, which is used to form a composite vector to represent the given state of centralities. The performance of the proposed method is evaluated through simulated predictions on data sets from real mobile networks. Results indicate significantly low prediction error rate occurs, with a suitable implementation of the proposed method.
Hex-Cell is an interconnection network that has attractive features like the embedding capability of topological structures; such as; bus, ring, tree and mesh topologies. In this paper, we present two algorithms for embedding bus and ring topologies onto Hex-Cell interconnection network. We use three metrics to evaluate our proposed algorithms: dilation, congestion, and expansion. Our evaluation results
show that the congestion of our two proposed algorithms is equal to one; and the dilation is equal to 2d-1 for the first algorithm and 1 for the second.
Hybrid multi objectives genetic algorithms and immigrants scheme for dynamic ...khalil IBRAHIM
the main concept of intelligent optimization techniques, artificial neural networks, and new genetic algorithms to solve the multi-objective multicast routing problems with shortest path (SP) problem that used in the addresses networks and improve all processes addressing in the wireless communications based on multi-objective optimization. The most important characteristics in mobile wireless networks is the topology dynamics and the network topology changes over time, the routing problem (SPRP) in mobile ad hoc networks (MANETs) turns out to be a dynamic optimization problem[13], the hybrid immigrants multiple-objective genetic algorithm (HIMOGAs) in the real- world are dynamic in nature, that has objective functions, constraints, and parameters, the dynamic optimization problems (DOPs) are a big challenges to evolutionary multi-objective, since any environmental change may affect the objective vector, constraints, and parameters, HIMOGA for the optimization goal is to track the moving of parameters and get a sequence of approximations solutions over time. The quantity of services (QoS) is supporting guarantee for all data traffic and getting the maximizing utilization for network, the QoS based on multicast routing offers significant challenges, and increases to use an efficient multicast routing protocol that will be able to check multicast routing and satisfying QoS constraints, The author propose to use HIMOGAs and SP algorithm to solve multicast problem that produces new generation wireless networks with immigrants schema to get high-quality solutions after each change and satisfying all objectives.
Hybrid iterated local search algorithm for optimization route of airplane tr...IJECEIAES
The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the TabuSA algorithm.
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost
pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of
optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in
transportation field due to the spatial nature of such problems. In this context, we couple a geographical
information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical
solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
INTEGRATION OF GIS AND OPTIMIZATION ROUTINES FOR THE VEHICLE ROUTING PROBLEMijccmsjournal
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in transportation field due to the spatial nature of such problems. In this context, we couple a geographical information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
Improved algorithm for road region segmentation based on sequential monte car...csandit
In recent years, many researchers and car makers put a lot of intensive effort into development
of autonomous driving systems. Since visual information is the main modality used by human
driver, a camera mounted on moving platform is very important kind of sensor, and various
computer vision algorithms to handle vehicle surrounding situation are under intensive
research. Our final goal is to develop a vision based lane detection system with ability to
handle various types of road shapes, working on both structured and unstructured roads, ideally
under presence of shadows. This paper presents a modified road region segmentation algorithm
based on sequential Monte-Carlo estimation. Detailed description of the algorithm is given,
and evaluation results show that the proposed algorithm outperforms the segmentation
algorithm developed as a part of our previous work, as well as an conventional algorithm based
on colour histogram.
This work considers the multi-objective optimization problem constrained by a system of bipolar fuzzy relational equations with max-product composition. An integer optimization based technique for order of preference by similarity to the ideal solution is proposed for solving such a problem. Some critical features associated with the feasible domain and optimal solutions of the bipolar max-Tp equation constrained optimization problem are studied. An illustrative example verifying the idea of this paper is included. This
is the first attempt to study the bipolar max-T equation constrained multi-objective optimization problems
from an integer programming viewpoint.
Bit Error Rate Analysis in Multicast Multiple Input Multiple Output Systemsrahulmonikasharma
At the present time whole information and communication technology industry contributes to the global carbon emission. With the aim of reducing the carbon footprint and the operating cost of wireless networks, overall energy reduction is required in the region of two to three orders of magnitude. Meanwhile, significant increase of the network spectrum efficiency is needed to cope with the exponentially increasing traffic loads. Due to this factors spatial modulation (SM) has recently established itself as promising transmission concept which belongs to single-radio frequency large scale multiple input multiple output (MIMO) wireless system. Spatial modulation MIMO takes advantage of whole antenna array at the transmitter, while using limited number of radio frequency chains. The multiple input multiple output multiplies capacity by transmitting different signals over multiple antennas and orthogonal frequency division multiplexing (OFDM), which divides a radio channel into many closely spaced sub channels to provide more reliable communication at high speeds. The system calculate the bit error rate (BER) for multicast multiple input multiple output system with the spatial modulation (SM) and study the effect of signal to noise ratio on bit error rate. MATLAB software is use to simulate system. The simulation results show that bit error rate decreases as signal to noise ratio increases. System reaches zero bit error rate for the value of signal to noise ratio greater than 18dB. System has provided less bit error rate for large signal to noise ratio which improves system performance.
Centrality Prediction in Mobile Social NetworksIJERA Editor
By analyzing evolving centrality roles using time dependent graphs, researchers may predict future centrality values. This may prove invaluable in designing efficient routing and energy saving strategies and have profound implications on evolving social behavior in dynamic social networks. In this paper, we propose a new method to predict centrality values of nodes in a dynamic environment. The proposed method is based on calculating the correlation between current and past measure of centrality for each corresponding node, which is used to form a composite vector to represent the given state of centralities. The performance of the proposed method is evaluated through simulated predictions on data sets from real mobile networks. Results indicate significantly low prediction error rate occurs, with a suitable implementation of the proposed method.
Hex-Cell is an interconnection network that has attractive features like the embedding capability of topological structures; such as; bus, ring, tree and mesh topologies. In this paper, we present two algorithms for embedding bus and ring topologies onto Hex-Cell interconnection network. We use three metrics to evaluate our proposed algorithms: dilation, congestion, and expansion. Our evaluation results
show that the congestion of our two proposed algorithms is equal to one; and the dilation is equal to 2d-1 for the first algorithm and 1 for the second.
Hybrid multi objectives genetic algorithms and immigrants scheme for dynamic ...khalil IBRAHIM
the main concept of intelligent optimization techniques, artificial neural networks, and new genetic algorithms to solve the multi-objective multicast routing problems with shortest path (SP) problem that used in the addresses networks and improve all processes addressing in the wireless communications based on multi-objective optimization. The most important characteristics in mobile wireless networks is the topology dynamics and the network topology changes over time, the routing problem (SPRP) in mobile ad hoc networks (MANETs) turns out to be a dynamic optimization problem[13], the hybrid immigrants multiple-objective genetic algorithm (HIMOGAs) in the real- world are dynamic in nature, that has objective functions, constraints, and parameters, the dynamic optimization problems (DOPs) are a big challenges to evolutionary multi-objective, since any environmental change may affect the objective vector, constraints, and parameters, HIMOGA for the optimization goal is to track the moving of parameters and get a sequence of approximations solutions over time. The quantity of services (QoS) is supporting guarantee for all data traffic and getting the maximizing utilization for network, the QoS based on multicast routing offers significant challenges, and increases to use an efficient multicast routing protocol that will be able to check multicast routing and satisfying QoS constraints, The author propose to use HIMOGAs and SP algorithm to solve multicast problem that produces new generation wireless networks with immigrants schema to get high-quality solutions after each change and satisfying all objectives.
Hybrid iterated local search algorithm for optimization route of airplane tr...IJECEIAES
The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the TabuSA algorithm.
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost
pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of
optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in
transportation field due to the spatial nature of such problems. In this context, we couple a geographical
information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical
solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
INTEGRATION OF GIS AND OPTIMIZATION ROUTINES FOR THE VEHICLE ROUTING PROBLEMijccmsjournal
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in transportation field due to the spatial nature of such problems. In this context, we couple a geographical information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
This paper deals with the optimization of the capacity of a terminal railway station using the
Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the
number of trains that depart from the station in unit interval of time. The railway capacity
optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP),
with the TSP nodes representing the train arrival /departure events and the TSP total cost
representing the total time-interval of the schedule. The application problem is then optimized
using the ACO algorithm. The simulation experiments validate the formulation of the railway
capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those
produced by the domain experts.
This paper deals with the optimization of the capacity of a terminal railway station using the Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the
number of trains that depart from the station in unit interval of time. The railway capacity
optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP),
with the TSP nodes representing the train arrival /departure events and the TSP total cost
representing the total time-interval of the schedule. The application problem is then optimized
using the ACO algorithm. The simulation experiments validate the formulation of the railway
capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those
produced by the domain experts.
Quantum inspired evolutionary algorithm for solving multiple travelling sales...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
The well-known Vehicle Routing Problem (VRP) consist of assigning routeswith a set
ofcustomersto different vehicles, in order tominimize the cost of transport, usually starting from a central
warehouse and using a fleet of fixed vehicles. There are numerousapproaches for the resolution of this kind of
problems, being the metaheuristic techniques the most used, including the Genetic Algorithms (AG). The
number of approachesto the different parameters of an AG (selection, crossing, mutation...) in the literature is
such that it is not easy to take a resolution of a VRP problem directly. This paper aims to simplify this task by
analyzing the best known approaches with standard VRP data sets, and showing the parameter configurations
that offer the best results.
The railway capacity optimization problem deals with the maximization of the number of trains running on
a given network per unit time. In this study, we frame this problem as a typical asymmetrical Travelling
Salesman Problem (ATSP), with the ATSP nodes representing the train arrival /departure events and the
ATSP total cost representing the total time-interval of the schedule. The application problem is then
optimized using the standard Ant Colony Optimization (ACO) algorithm. The simulation experiments
validate the formulation of the railway capacity problem as an ATSP and the ACO algorithm produces
optimal solutions superior to those produced by the domain experts.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
Using social media in education provides learners with an informal way for communication. Informal communication tends to remove barriers and hence promotes student engagement. This paper presents our experience in using three different social media technologies in teaching software project management course. We conducted different surveys at the end of every semester to evaluate students’ satisfaction and engagement. Results show that using social media enhances students’ engagement and satisfaction. However, familiarity with the tool is an important factor for student satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The amount of piracy in the streaming digital content in general and the music industry in specific is posing a real challenge to digital content owners. This paper presents a DRM solution to monetizing, tracking and controlling online streaming content cross platforms for IP enabled devices. The paper benefits from the current advances in Blockchain and cryptocurrencies. Specifically, the paper presents a Global Music Asset Assurance (GoMAA) digital currency and presents the iMediaStreams Blockchain to enable the secure dissemination and tracking of the streamed content. The proposed solution provides the data owner the ability to control the flow of information even after it has been released by creating a secure, selfinstalled, cross platform reader located on the digital content file header. The proposed system provides the content owners’ options to manage their digital information (audio, video, speech, etc.), including the tracking of the most consumed segments, once it is release. The system benefits from token distribution between the content owner (Music Bands), the content distributer (Online Radio Stations) and the content consumer(Fans) on the system blockchain.
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This paper discusses the importance of verb suffix mapping in Discourse translation system. In
discourse translation, the crucial step is Anaphora resolution and generation. In Anaphora
resolution, cohesion links like pronouns are identified between portions of text. These binders
make the text cohesive by referring to nouns appearing in the previous sentences or nouns
appearing in sentences after them. In Machine Translation systems, to convert the source
language sentences into meaningful target language sentences the verb suffixes should be
changed as per the cohesion links identified. This step of translation process is emphasized in
the present paper. Specifically, the discussion is on how the verbs change according to the
subjects and anaphors. To explain the concept, English is used as the source language (SL) and
an Indian language Telugu is used as Target language (TL)
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The using of information technology resources is rapidly increasing in organizations,
businesses, and even governments, that led to arise various attacks, and vulnerabilities in the
field. All resources make it a must to do frequently a penetration test (PT) for the environment
and see what can the attacker gain and what is the current environment's vulnerabilities. This
paper reviews some of the automated penetration testing techniques and presents its
enhancement over the traditional manual approaches. To the best of our knowledge, it is the
first research that takes into consideration the concept of penetration testing and the standards
in the area.This research tackles the comparison between the manual and automated
penetration testing, the main tools used in penetration testing. Additionally, compares between
some methodologies used to build an automated penetration testing platform.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
In order to treat and analyze real datasets, fuzzy association rules have been proposed. Several
algorithms have been introduced to extract these rules. However, these algorithms suffer from
the problems of utility, redundancy and large number of extracted fuzzy association rules. The
expert will then be confronted with this huge amount of fuzzy association rules. The task of
validation becomes fastidious. In order to solve these problems, we propose a new validation
method. Our method is based on three steps. (i) We extract a generic base of non redundant
fuzzy association rules by applying EFAR-PN algorithm based on fuzzy formal concept analysis.
(ii) we categorize extracted rules into groups and (iii) we evaluate the relevance of these rules
using structural equation model.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
2. 60 Computer Science & Information Technology (CS & IT)
of relevant techniques that consider routes, frequencies and other network parameters, based on
preset objective functions, which are to be optimized. Widely used approaches include Genetic
Algorithms (GA) [3], Simulated Annealing [4] and Ant Colony Optimization [5] besides
The Particle Swarm Optimization (PSO) is one of the most effective evolutionary algorithms
inspired from social behavior of animals [6]. Its simplicity and efficiency makes this algorithm
very popular. Due to these advantages, the PSO algorithm h
such as medical diagnosis, grid scheduling, robot path planning and computer vision. This
algorithm is capable of solving problems with continuous search spaces, while some problems
have discrete search spaces. The binary ve
TRNDP belongs to the discrete problems and probably this is the reason that the PSO algorithm
has not been applied to this problem so far.
The rest of the paper presents the formulation of the TRNDP problem so
procedures can be used to approach its solution. The paper is structured as follows: section 2
analyses the formulation of the problem. Section 3 describes the component architecture of the
proposed framework. Section 4 presents the c
2. PROBLEM FORMULATION
This work has been based on the assumptions that there is a fixed number of
number of bus lines L and that the bus lines have a maximum number of
solution is represented by a binary two dimensional matrix of
represents the l-th bus line. A “1” in position (l,
the σ-th bus stop, while a “0” represents that the
solution must be in vector form, therefore, we vectorize the 2D matrix to formulate the hereafter
mentioned as “LN” vector with
bits to encode bus frequencies per line (number of bits depends on what is the maximum bus
frequency) hereafter denoted as “
bus hereafter denoted as “G”. The following step is to minimize the objective function gi
where Du is the unsatisfied passenger demand (not served under maximum transfers),
average travel time, e the pollution emissions
number of conventional vehicles and
- w6 are defined according to the policy we want to implement or according to values that can be
statistically estimated. All the above quantities are straightforward to compute given
However, the question is: given the solution vector how do we define the sequence of the bus
stops? Clearly the solution vector does not really capture the sequence in which the bus stops are
visited. This is done deliberately and is one of our major contribut
reduce the solution space. E.g. for
which we are seeking optimum is ~10
this reduces the search space to ~10
Computer Science & Information Technology (CS & IT)
ques that consider routes, frequencies and other network parameters, based on
preset objective functions, which are to be optimized. Widely used approaches include Genetic
Algorithms (GA) [3], Simulated Annealing [4] and Ant Colony Optimization [5] besides
The Particle Swarm Optimization (PSO) is one of the most effective evolutionary algorithms
inspired from social behavior of animals [6]. Its simplicity and efficiency makes this algorithm
very popular. Due to these advantages, the PSO algorithm has been applied to many domains
such as medical diagnosis, grid scheduling, robot path planning and computer vision. This
algorithm is capable of solving problems with continuous search spaces, while some problems
have discrete search spaces. The binary version of PSO (BPSO) was proposed by [7]. The
TRNDP belongs to the discrete problems and probably this is the reason that the PSO algorithm
has not been applied to this problem so far.
The rest of the paper presents the formulation of the TRNDP problem so that PSO optimization
procedures can be used to approach its solution. The paper is structured as follows: section 2
analyses the formulation of the problem. Section 3 describes the component architecture of the
proposed framework. Section 4 presents the conclusions of this work and the future perspective.
ORMULATION
This work has been based on the assumptions that there is a fixed number of S bus stops, a fixed
and that the bus lines have a maximum number of s bus stops
solution is represented by a binary two dimensional matrix of L rows and s columns. The
th bus line. A “1” in position (l, σ) represents that the l-th bus line goes through
th bus stop, while a “0” represents that the bus line does not include the bus 2stop. The
solution must be in vector form, therefore, we vectorize the 2D matrix to formulate the hereafter
” vector with L × s elements. To the previous vector we also have to append
equencies per line (number of bits depends on what is the maximum bus
frequency) hereafter denoted as “f” and whether the line is operated by electric or conventional
”. The following step is to minimize the objective function gi
is the unsatisfied passenger demand (not served under maximum transfers),
the pollution emissions Ncs the number of charging stations, V
number of conventional vehicles and Ve the required number of electric vehicles. The weights w
are defined according to the policy we want to implement or according to values that can be
statistically estimated. All the above quantities are straightforward to compute given
ver, the question is: given the solution vector how do we define the sequence of the bus
stops? Clearly the solution vector does not really capture the sequence in which the bus stops are
visited. This is done deliberately and is one of our major contributions, because we significantly
reduce the solution space. E.g. for S = 50 and s = 10 the number of possible permutations in
which we are seeking optimum is ~1016
, while if we ignore the permutations as in our method
this reduces the search space to ~1011
.
ques that consider routes, frequencies and other network parameters, based on
preset objective functions, which are to be optimized. Widely used approaches include Genetic
Algorithms (GA) [3], Simulated Annealing [4] and Ant Colony Optimization [5] besides others.
The Particle Swarm Optimization (PSO) is one of the most effective evolutionary algorithms
inspired from social behavior of animals [6]. Its simplicity and efficiency makes this algorithm
as been applied to many domains
such as medical diagnosis, grid scheduling, robot path planning and computer vision. This
algorithm is capable of solving problems with continuous search spaces, while some problems
rsion of PSO (BPSO) was proposed by [7]. The
TRNDP belongs to the discrete problems and probably this is the reason that the PSO algorithm
that PSO optimization
procedures can be used to approach its solution. The paper is structured as follows: section 2
analyses the formulation of the problem. Section 3 describes the component architecture of the
onclusions of this work and the future perspective.
bus stops, a fixed
bus stops. The
rows and s columns. The l-th row
th bus line goes through
bus line does not include the bus 2stop. The
solution must be in vector form, therefore, we vectorize the 2D matrix to formulate the hereafter
elements. To the previous vector we also have to append
equencies per line (number of bits depends on what is the maximum bus
” and whether the line is operated by electric or conventional
”. The following step is to minimize the objective function given by:
is the unsatisfied passenger demand (not served under maximum transfers), T the
Vc the required
the required number of electric vehicles. The weights w1
are defined according to the policy we want to implement or according to values that can be
statistically estimated. All the above quantities are straightforward to compute given LN, F, G.
ver, the question is: given the solution vector how do we define the sequence of the bus
stops? Clearly the solution vector does not really capture the sequence in which the bus stops are
ions, because we significantly
= 10 the number of possible permutations in
, while if we ignore the permutations as in our method
3. Computer Science &
The answer is given by assuming that the next bus stop is the one closest to the current one. In
other words we need to define the path that covers all the bus stops and at the same time has the
minimum possible length. The answer to this problem is giv
solves exactly this problem [8].
3. THE BINARY PSO ALGORITHM
The binary version of PSO (BPSO) was proposed by [7]. The continuous and binary versions of
PSO are distinguished by two different components: the transfer function and the different
position updating procedure. The transfer function is used to map a conti
binary one, and the updating process is designed to switch positions of particles between 0 and 1
in binary search spaces. Several solutions have been proposed to the problem of getting trapped
in local minima, e.g., [10], [11]. In
and s-shaped were investigated. Let’s start from the continuous PSO. Each particle
corresponds to a single solution
consider the current position, the current velocity
pbest, and the distance to the global best solution,
where w is a weighting function,
coefficients. In the next iteration the particle will evolve to:
In binary space, due to dealing with only two numbers (‘‘0’’ and ‘‘1’’), the position upd
process cannot be performed using eq. (3). Therefore, another definition of velocities is needed
for changing positions from ‘‘0’’ to ‘‘1’’ or vice versa. This can be done by redefining the
velocity to be the probability of a bit taking the value
(4) was employed in [1] to transform all real values of velocities to probability values in the
interval [0,1].
where vi
k
(t ) indicates the k-th dimension of the velocity vector. Then the position vectors a
updated according to the following:
where r is a random number in the interval [0, 1]. Variations of this strategy have been proposed
in [10], [11], [12].
Computer Science & Information Technology (CS & IT)
The answer is given by assuming that the next bus stop is the one closest to the current one. In
other words we need to define the path that covers all the bus stops and at the same time has the
minimum possible length. The answer to this problem is given by the Hamiltonian path, which
LGORITHM
The binary version of PSO (BPSO) was proposed by [7]. The continuous and binary versions of
PSO are distinguished by two different components: the transfer function and the different
position updating procedure. The transfer function is used to map a continuous search space to a
binary one, and the updating process is designed to switch positions of particles between 0 and 1
in binary search spaces. Several solutions have been proposed to the problem of getting trapped
in local minima, e.g., [10], [11]. In [12], two different families of transfer functions, v
shaped were investigated. Let’s start from the continuous PSO. Each particle
corresponds to a single solution xi(t). To evolve towards a better solution the particle has to
consider the current position, the current velocity vi(t), the distance to their personal best solution,
, and the distance to the global best solution, gbest. This is formulated as follows:
where w is a weighting function, r1, r2 ∈ [0,1] are random numbers and c1, c2 are acceleration
coefficients. In the next iteration the particle will evolve to:
In binary space, due to dealing with only two numbers (‘‘0’’ and ‘‘1’’), the position upd
process cannot be performed using eq. (3). Therefore, another definition of velocities is needed
for changing positions from ‘‘0’’ to ‘‘1’’ or vice versa. This can be done by redefining the
velocity to be the probability of a bit taking the value 0 or 1. A sigmoid transfer function as in eq.
(4) was employed in [1] to transform all real values of velocities to probability values in the
th dimension of the velocity vector. Then the position vectors a
updated according to the following:
where r is a random number in the interval [0, 1]. Variations of this strategy have been proposed
61
The answer is given by assuming that the next bus stop is the one closest to the current one. In
other words we need to define the path that covers all the bus stops and at the same time has the
en by the Hamiltonian path, which
The binary version of PSO (BPSO) was proposed by [7]. The continuous and binary versions of
PSO are distinguished by two different components: the transfer function and the different
nuous search space to a
binary one, and the updating process is designed to switch positions of particles between 0 and 1
in binary search spaces. Several solutions have been proposed to the problem of getting trapped
[12], two different families of transfer functions, v- shaped
shaped were investigated. Let’s start from the continuous PSO. Each particle i at time t
. To evolve towards a better solution the particle has to
, the distance to their personal best solution,
. This is formulated as follows:
are acceleration
In binary space, due to dealing with only two numbers (‘‘0’’ and ‘‘1’’), the position updating
process cannot be performed using eq. (3). Therefore, another definition of velocities is needed
for changing positions from ‘‘0’’ to ‘‘1’’ or vice versa. This can be done by redefining the
. A sigmoid transfer function as in eq.
(4) was employed in [1] to transform all real values of velocities to probability values in the
th dimension of the velocity vector. Then the position vectors are
where r is a random number in the interval [0, 1]. Variations of this strategy have been proposed
4. 62 Computer Science & Information Technology (CS & IT)
4. COMPONENT ARCHITECTURE
The design of our case study is based on
middleware and computational analysis, namely: HTML, Javascript, Google Maps API at the
presentation tier, Hypertext Preprocessor (PHP), the known dynamic programming language for
the middleware and Octave / Ma
the systems includes a graphical web interface capable of displaying the graph of the problem
realistically and in real time through the programming interface of Google Maps engine
management. The graph presented by Google Maps is processed by means of a computational
analysis module which implement the TRNDP solver based on the Octave and Matlab
environments. Middleware logic, is capable to handle requests directed towards the graphical
interface, direct them to the computational analysis module and return results in appropriate form
to be presented by the maps presentation engine.
Fig. 1: an initial configuration of the system that is, a realistic selection of graph nodes and routes can be
displayed on Google Maps and also information about distances and traffic can be retrieved and passed to
the computational analysis system.
The Google Maps map management and presentation engine offers programming interfaces that
are compatible with various programming languages. For the purposes of our work, we took
advantage of the Javascript programming [9]. Necessary conditions for the use of the interface
has been the knowledge of web technologies and principles of object
design mode. This platform was chosen because it dominates the market of GIS and provides
free, stable and reliable access. Google maps also offer interesting features such as real time
traffic support, carbon dioxide emissions estimations, beside others. The use
interfaces of the platform service is offered by means of subscription and the acquisition of
appropriate application programming interface (API) keys that allow the service to monitor
usage. Typical facilities include the DistanceMatrix
between start and destination nodes, DirectionsService offering directional calculation service
between one or more locations, DirectionsRoute service offering route calculation between
departure and destination which contains the sections of the route, among others, Map objects
capable to illustrate maps. Features of the service include vehicle type specification, travel modes
that is bicycling, driving, transit, or walking. As a commercial product, the Google
allows limited use when not in subscription mode. In this context the design of our system took
into account the respective restrictions. When the design took place the former allowed 25,000
map loads per day for 90 consecutive days, recovering 10
Computer Science & Information Technology (CS & IT)
RCHITECTURE AND ALGORITHMIC APPROACH
The design of our case study is based on the combination of presentation technologies,
middleware and computational analysis, namely: HTML, Javascript, Google Maps API at the
presentation tier, Hypertext Preprocessor (PHP), the known dynamic programming language for
the middleware and Octave / Matlab for the computational analysis back-end. More specifically,
the systems includes a graphical web interface capable of displaying the graph of the problem
realistically and in real time through the programming interface of Google Maps engine
. The graph presented by Google Maps is processed by means of a computational
analysis module which implement the TRNDP solver based on the Octave and Matlab
environments. Middleware logic, is capable to handle requests directed towards the graphical
face, direct them to the computational analysis module and return results in appropriate form
to be presented by the maps presentation engine.
Fig. 1: an initial configuration of the system that is, a realistic selection of graph nodes and routes can be
displayed on Google Maps and also information about distances and traffic can be retrieved and passed to
The Google Maps map management and presentation engine offers programming interfaces that
us programming languages. For the purposes of our work, we took
advantage of the Javascript programming [9]. Necessary conditions for the use of the interface
has been the knowledge of web technologies and principles of object-oriented programming
ode. This platform was chosen because it dominates the market of GIS and provides
free, stable and reliable access. Google maps also offer interesting features such as real time
traffic support, carbon dioxide emissions estimations, beside others. The use of the programming
interfaces of the platform service is offered by means of subscription and the acquisition of
appropriate application programming interface (API) keys that allow the service to monitor
usage. Typical facilities include the DistanceMatrixService offering distance calculation service
between start and destination nodes, DirectionsService offering directional calculation service
between one or more locations, DirectionsRoute service offering route calculation between
n which contains the sections of the route, among others, Map objects
capable to illustrate maps. Features of the service include vehicle type specification, travel modes
that is bicycling, driving, transit, or walking. As a commercial product, the Google
allows limited use when not in subscription mode. In this context the design of our system took
into account the respective restrictions. When the design took place the former allowed 25,000
map loads per day for 90 consecutive days, recovering 100 elements each performed search
PPROACH
the combination of presentation technologies,
middleware and computational analysis, namely: HTML, Javascript, Google Maps API at the
presentation tier, Hypertext Preprocessor (PHP), the known dynamic programming language for
end. More specifically,
the systems includes a graphical web interface capable of displaying the graph of the problem
realistically and in real time through the programming interface of Google Maps engine
. The graph presented by Google Maps is processed by means of a computational
analysis module which implement the TRNDP solver based on the Octave and Matlab
environments. Middleware logic, is capable to handle requests directed towards the graphical
face, direct them to the computational analysis module and return results in appropriate form
Fig. 1: an initial configuration of the system that is, a realistic selection of graph nodes and routes can be
displayed on Google Maps and also information about distances and traffic can be retrieved and passed to
The Google Maps map management and presentation engine offers programming interfaces that
us programming languages. For the purposes of our work, we took
advantage of the Javascript programming [9]. Necessary conditions for the use of the interface
oriented programming
ode. This platform was chosen because it dominates the market of GIS and provides
free, stable and reliable access. Google maps also offer interesting features such as real time
of the programming
interfaces of the platform service is offered by means of subscription and the acquisition of
appropriate application programming interface (API) keys that allow the service to monitor
Service offering distance calculation service
between start and destination nodes, DirectionsService offering directional calculation service
between one or more locations, DirectionsRoute service offering route calculation between
n which contains the sections of the route, among others, Map objects
capable to illustrate maps. Features of the service include vehicle type specification, travel modes
that is bicycling, driving, transit, or walking. As a commercial product, the Google Maps API
allows limited use when not in subscription mode. In this context the design of our system took
into account the respective restrictions. When the design took place the former allowed 25,000
0 elements each performed search
5. Computer Science & Information Technology (CS & IT) 63
(query), recovering 100 elements per 10 seconds, recovery of 2,500 items per 24 hours. It is
worth noting that requests are also subject to rate limits. The design of the system took into
account all the above restrictions in order for the system to be capable to represent nodes (stops)
as points in Google Maps, represent of realistic routes ie routes that take account of actual
characteristics as e.g. one-way. An initial configuration of the system is illustrated in fig. 1.
GNU Octave is a high-level programming language, primarily intended for numerical
computations. It offers a command line interface for solving linear and nonlinear problems
numerically, and for performing other numerical experiments using an interpreted language
mostly compatible with the well known MATLAB platform by Mathworks. It can also be used as
a language oriented script execution. Octave is free software, distributed under the terms of the
GNU General Public License. Besides its use for desktop computers that is, for personal
scientific calculations, Octave is also used in academia and industry. Its features include that it is
written in C ++ and uses the standard C ++ library, it uses an interpreter to execute the script
language and it is expandable with the use of dynamic parts (modules). Versions 3.8.0 and later
include a graphical user interface (GUI), other than the traditional command line interface (CLI).
The architecture of our solution employs open source PSO packages compatible with Octave.
The architectural components and their integration was a challenge for the implementation of the
system. Google maps was a valuable and suitable solution since it offers unique functionality,
satisfactory front-end interface and sufficient, usable APIs. Matlab is both capable and efficient
to execute the algorithmic logic but, by design, not suitable for the middle tier of the platform;
Octave on the contrary is very easy to integrate but does not support either PSO or GA to the
desirable extent. In order to produce realistic, applicable solutions the system needs to produce
meaningful routes; to this end they needed to be optimized with respect to their total distance.
Thus, besides optimizing with respect to the objective function analyzed above, it was decided to
6. 64 Computer Science & Information Technology (CS & IT)
recover the shortest possible route that visits each node exactly once and returns to the origin.
The aforementioned sentence is an expression of the Traveling Salesman problem [13] the well
known non-deterministic polynomial time (NP) hard problem. This logic also needed to run in
the middle tier.
Fig. 2: in order to ensure that bus routes are optimal with respect to traveling distance the systems
solves the traveling salesman problem using a platform independent implementation of a genetic
algorithm [14], illustrated in the figure.
5. CONCLUSIONS AND FUTURE WORK
The current paper outlines the engineering and algorithmic design of the DIANNA system that
aims to solve the TRNDP problem using a PSO approach. The objective function of the
optimization algorithm is very similar to [3]; it ultimately aims to produce solutions that optimize
environmental parameters that is, vehicle emissions and vehicle types (electrical or conventional)
besides more typical parameters of the problem such as distances, bus frequencies, demand. The
design is innovative since the formulation of the solution is binary, designed to facilitate easy
manipulation. Also, the architecture of the informational system is designed to interact with well
known GIS services, relies on middleware logic that executes the optimization and presents the
results using web technologies. Fig. 2 illustrates the implementation of a genetic algorithm that
solves the traveling salesman problem; the implementation relies in platform independent server-
side logic that can be called by any component of the system.
In the near future we expect to produce experimental results that exhibit realistic solutions for the
case of Heraklion, Crete and, since the formulation of the problem allows it, we also expect to
investigate the extent to which environmental policies can be applied that is, find optimal or next
to optimal values for parameters w1 to w6 that correspond to minimum environmental costs.
ACKNOWLEDGEMENTS
This work is part of the DIANA project, funded by the Operational Program "Education and
Lifelong Learning", action Archimedes III, co-financed by the EU and National funds (National
Strategic Reference Framework 2007-2013).
REFERENCES
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on September 1, 2015
AUTHORS
Michael Kalochristianakis
Michael is currently an associate lecturer and researcher at the Technological
Educational Institution of Crete (TEIC), at the Department of Informatics
Engineering. His interests include but are not limited to technical project
management and project management in general, innovative IT systems and
services, software development methodologies, multimedia technologies and
algorithms, web engineering, object oriented design, architectures, frameworks
and patterns, in-band management and remote management, web and portal
development, open source technologies, green technologies and energy
conservation. Michael holds a Doctorate degree in Computer Engineering and Informatics, a Masters
degree in Computer Science and a Diploma in Electrical Engineering and Computer Technology.
Dimitrios Kosmopoulos
Dimitrios Kosmopoulos is currently Assistant Professor at the University of
Patras, Department of Cultural Heritage and New Technologies. Previously he
was at Technical Educational Institute of Crete - Department of Informatics
Engineering and before that, he was at Rutgers University, Computer Science
Department, CBIM Lab and at the University of Texas at Arlington, Department
of Computer Science and Engineering. He has been a research scientist in the
Computational Intelligence Laboratory of the Institute of Informatics and
Telecommunications in the NCSR Demokritos. He was also affiliated with the
Department of Electrical and Computer Engineering of the National Technical
University of Athens (Greece). He was employed as a researcher and developer for
various companies and institutions.