This document discusses the effects of different migration periods on the Parallel Multi-Swarm Particle Swarm Optimization (PCLPSO) algorithm. PCLPSO is a parallel metaheuristic algorithm based on Particle Swarm Optimization and the Comprehensive Learning Particle Swarm Optimization. It uses multiple swarms that work cooperatively and concurrently. The migration period, which is the frequency at which swarms share information, is an important parameter. The document analyzes PCLPSO performance on benchmark functions using different migration periods, finding the best periods varied with problem dimension and type.
ANALYSINBG THE MIGRATION PERIOD PARAMETER IN PARALLEL MULTI-SWARM PARTICLE SW...ijcsit
In recent years, there has been an increasing interest in parallel computing. In parallel computing, multiple
computing resources are used simultaneously in solving a problem. There are multiple processors that will
work concurrently and the program is divided into different tasks to be simultaneously solved. Recently, a
considerable literature has grown up around the theme of metaheuristic algorithms. Particle swarm
optimization (PSO) algorithm is a popular metaheuristic algorithm. The parallel comprehensive learning
particle swarm optimization (PCLPSO) algorithm based on PSO has multiple swarms based on the masterslave
paradigm and works cooperatively and concurrently. The migration period is an important parameter
in PCLPSO and affects the efficiency of the algorithm. We used the well-known benchmark functions in the
experiments and analysed the performance of PCLPSO using different migration periods.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve
scheduling.
In this paper, a combination of genetic algorithms and binary gravitational attraction is used for scheduling problem solving, where the
reduction in the duty performance timing and cost-effective use of simultaneous resources are investigated. In this case, the user
determines the execution time parameter and cost-effective use of resources. In this algorithm, a new approach that has led to a
balanced load of resources is used in the selection of resources. Experimental results reveals that our proposed algorithm in terms of
cost-time and selection of the best resource has reached better results than other algorithm.
Job Scheduling on the Grid Environment using Max-Min Firefly AlgorithmEditor IJCATR
Grid computing indeed is the next generation of distributed systems and its goals is creating a powerful virtual, great, and
autonomous computer that is created using countless Heterogeneous resource with the purpose of sharing resources. Scheduling is one
of the main steps to exploit the capabilities of emerging computing systems such as the grid. Scheduling of the jobs in computational
grids due to Heterogeneous resources is known as an NP-Complete problem. Grid resources belong to different management domains
and each applies different management policies. Since the nature of the grid is Heterogeneous and dynamic, techniques used in
traditional systems cannot be applied to grid scheduling, therefore new methods must be found. This paper proposes a new algorithm
which combines the firefly algorithm with the Max-Min algorithm for scheduling of jobs on the grid. The firefly algorithm is a new
technique based on the swarm behavior that is inspired by social behavior of fireflies in nature. Fireflies move in the search space of
problem to find the optimal or near-optimal solutions. Minimization of the makespan and flowtime of completing jobs simultaneously
are the goals of this paper. Experiments and simulation results show that the proposed method has a better efficiency than other
compared algorithms.
ANALYSINBG THE MIGRATION PERIOD PARAMETER IN PARALLEL MULTI-SWARM PARTICLE SW...ijcsit
In recent years, there has been an increasing interest in parallel computing. In parallel computing, multiple
computing resources are used simultaneously in solving a problem. There are multiple processors that will
work concurrently and the program is divided into different tasks to be simultaneously solved. Recently, a
considerable literature has grown up around the theme of metaheuristic algorithms. Particle swarm
optimization (PSO) algorithm is a popular metaheuristic algorithm. The parallel comprehensive learning
particle swarm optimization (PCLPSO) algorithm based on PSO has multiple swarms based on the masterslave
paradigm and works cooperatively and concurrently. The migration period is an important parameter
in PCLPSO and affects the efficiency of the algorithm. We used the well-known benchmark functions in the
experiments and analysed the performance of PCLPSO using different migration periods.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve
scheduling.
In this paper, a combination of genetic algorithms and binary gravitational attraction is used for scheduling problem solving, where the
reduction in the duty performance timing and cost-effective use of simultaneous resources are investigated. In this case, the user
determines the execution time parameter and cost-effective use of resources. In this algorithm, a new approach that has led to a
balanced load of resources is used in the selection of resources. Experimental results reveals that our proposed algorithm in terms of
cost-time and selection of the best resource has reached better results than other algorithm.
Job Scheduling on the Grid Environment using Max-Min Firefly AlgorithmEditor IJCATR
Grid computing indeed is the next generation of distributed systems and its goals is creating a powerful virtual, great, and
autonomous computer that is created using countless Heterogeneous resource with the purpose of sharing resources. Scheduling is one
of the main steps to exploit the capabilities of emerging computing systems such as the grid. Scheduling of the jobs in computational
grids due to Heterogeneous resources is known as an NP-Complete problem. Grid resources belong to different management domains
and each applies different management policies. Since the nature of the grid is Heterogeneous and dynamic, techniques used in
traditional systems cannot be applied to grid scheduling, therefore new methods must be found. This paper proposes a new algorithm
which combines the firefly algorithm with the Max-Min algorithm for scheduling of jobs on the grid. The firefly algorithm is a new
technique based on the swarm behavior that is inspired by social behavior of fireflies in nature. Fireflies move in the search space of
problem to find the optimal or near-optimal solutions. Minimization of the makespan and flowtime of completing jobs simultaneously
are the goals of this paper. Experiments and simulation results show that the proposed method has a better efficiency than other
compared algorithms.
Multiprocessor scheduling of dependent tasks to minimize makespan and reliabi...ijfcstjournal
Algorithms developed for scheduling applications on heterogeneous multiprocessor system focus on a
single objective such as execution time, cost or total data transmission time. However, if more than one
objective (e.g. execution cost and time, which may be in conflict) are considered, then the problem becomes
more challenging. This project is proposed to develop a multiobjective scheduling algorithm using
Evolutionary techniques for scheduling a set of dependent tasks on available resources in a multiprocessor
environment which will minimize the makespan and reliability cost. A Non-dominated sorting Genetic
Algorithm-II procedure has been developed to get the pareto- optimal solutions. NSGA-II is a Elitist
Evolutionary algorithm, and it takes the initial parental solution without any changes, in all iteration to
eliminate the problem of loss of some pareto-optimal solutions.NSGA-II uses crowding distance concept to
create a diversity of the solutions.
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..
Biogeography Based Optimization (BBO) is a new evolutionary algorithm for global optimization that was introduced in
2008. BBO is an application of biogeography to evolutionary algorithms. Biogeography is the study of the distribution of biodiversity
over space and time. It aims to analyze where organisms live, and in what abundance. BBO has certain features in common with other population-based optimization methods. Like GA and PSO, BBO can share information between solutions. This makes BBO applicable to many of the same types of problems that GA and PSO are used for, including unimodal, multimodal and deceptive functions. This paper explains the methodology of application of BBO algorithm for the constrained task scheduling problems.
EVOLVING CONNECTION WEIGHTS FOR PATTERN STORAGE AND RECALL IN HOPFIELD MODEL ...ijsc
In this paper, implementation of a genetic algorithm has been described to store and later, recall of some
prototype patterns in Hopfield neural network associative memory. Various operators of genetic algorithm
(mutation, cross-over, elitism etc) are used to evolve the population of optimal weight matrices for the
purpose of storing the patterns and then recalling of the patterns with induced noise was made, again using
a genetic algorithm. The optimal weight matrices obtained during the training are used as seed for starting
the GA in recalling, instead starting with random weight matrix. A detailed study of the comparison of
results thus obtained with the earlier results has been done. It has been observed that for Hopfield neural
networks, recall of patterns is more successful if evolution of weight matrices is applied for training
purpose also.
Efficient steganography techniques are needed for the security of digital information over the Internet and for secret data communication. Therefore, many techniques are proposed for steganography. One of these intelligent techniques is Particle Swarm Optimization (PSO) algorithm. Recently, many modifications are made to Standard PSO (SPSO) such as Human-Based Particle Swarm Optimization (HPSO). Therefore, this paper presents image steganography using HPSO in order to find best locations in image cover to hide text secret message. Then, a comparison is done between image steganography using PSO and using HPSO. Experimental results on six (256×256) cover images and different size of secret massages, prove that the performance of the proposed image steganography using HPSO has been improved in comparison with using SPSO.
Scheduling Using Multi Objective Genetic Algorithmiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Proposing a Scheduling Algorithm to Balance the Time and Energy Using an Impe...Editor IJCATR
Computational grids have become an appealing research area as they solve compute-intensive problems within the scientific
community and in industry. A grid computational power is aggregated from a huge set of distributed heterogeneous workers; hence, it
is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Unfortunately, current grid
schedulers suffer from the haste problem, which is the schedule inability to successfully allocate all input tasks. Accordingly, some tasks
fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously
allocated to other peers. This paper presents an imperialist competition algorithm (ICA) method to solve the grid scheduling problems.
The objective is to minimize the makespan and energy of the grid. Simulation results show that the grid scheduling problem can be
solved efficiently by the proposed method
An Effective PSO-inspired Algorithm for Workflow Scheduling IJECEIAES
The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers and storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on maximizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. This entails the need to adopt an effective scheduling algorithm in order to minimize task completion time (makespan). Although workflow scheduling has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this paper, we propose the LPSO, a novel algorithm for workflow scheduling problem that is based on the Particle Swarm Optimization method. Our proposed algorithm not only ensures a fast convergence but also prevents getting trapped in local extrema. We ran realistic scenarios using CloudSim and found that LPSO is superior to previously proposed algorithms and noticed that the deviation between the solution found by LPSO and the optimal solution is negligible.
Buffer Allocation Problem is an important research issue in manufacturing system design.
Objective of this paper is to find optimum buffer allocation for closed queuing network with
multi servers at each node. Sum of buffers in closed queuing network is constant. Attempt is
made to find optimum number of pallets required to maximize throughput of manufacturing
system which has pre specified space for allocating pallets. Expanded Mean Value Analysis is
used to evaluate the performance of closed queuing network. Particle Swarm Optimization is
used as generative technique to optimize the buffer allocation. Numerical experiments are
shown to explain effectiveness of procedure
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
MARKOV CHAIN AND ADAPTIVE PARAMETER SELECTION ON PARTICLE SWARM OPTIMIZERijsc
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic
behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in
the performance of PSO. As far as our investigation is concerned, most of the relevant researches are
based on computer simulations and few of them are based on theoretical approach. In this paper,
theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this
paper, which contains all the information needed for the future evolution. Then the memory-less property of
the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and
suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov
chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method
for parameter selection is proposed.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources.
The purpose of job scheduling in grid environment is to achieve high system throughput and minimize the execution time of applications.
The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively.
To obtain a good and efficient method to solve scheduling problems in grid, a new area of research is implemented. In this paper, a job
scheduling algorithm is proposed to assign jobs to available resources in grid environment. The proposed algorithm is based on Ant
Colony Optimization (ACO) algorithm. This algorithm is combined with one of the best scheduling algorithm, Suffrage. This paper uses
the result of Suffrage in proposed ACO algorithm. The main contribution of this work is to minimize the makespan of a given set of
jobs. The experimental results show that the proposed algorithm can lead to significant performance in grid environment.
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.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
Multiprocessor scheduling of dependent tasks to minimize makespan and reliabi...ijfcstjournal
Algorithms developed for scheduling applications on heterogeneous multiprocessor system focus on a
single objective such as execution time, cost or total data transmission time. However, if more than one
objective (e.g. execution cost and time, which may be in conflict) are considered, then the problem becomes
more challenging. This project is proposed to develop a multiobjective scheduling algorithm using
Evolutionary techniques for scheduling a set of dependent tasks on available resources in a multiprocessor
environment which will minimize the makespan and reliability cost. A Non-dominated sorting Genetic
Algorithm-II procedure has been developed to get the pareto- optimal solutions. NSGA-II is a Elitist
Evolutionary algorithm, and it takes the initial parental solution without any changes, in all iteration to
eliminate the problem of loss of some pareto-optimal solutions.NSGA-II uses crowding distance concept to
create a diversity of the solutions.
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..
Biogeography Based Optimization (BBO) is a new evolutionary algorithm for global optimization that was introduced in
2008. BBO is an application of biogeography to evolutionary algorithms. Biogeography is the study of the distribution of biodiversity
over space and time. It aims to analyze where organisms live, and in what abundance. BBO has certain features in common with other population-based optimization methods. Like GA and PSO, BBO can share information between solutions. This makes BBO applicable to many of the same types of problems that GA and PSO are used for, including unimodal, multimodal and deceptive functions. This paper explains the methodology of application of BBO algorithm for the constrained task scheduling problems.
EVOLVING CONNECTION WEIGHTS FOR PATTERN STORAGE AND RECALL IN HOPFIELD MODEL ...ijsc
In this paper, implementation of a genetic algorithm has been described to store and later, recall of some
prototype patterns in Hopfield neural network associative memory. Various operators of genetic algorithm
(mutation, cross-over, elitism etc) are used to evolve the population of optimal weight matrices for the
purpose of storing the patterns and then recalling of the patterns with induced noise was made, again using
a genetic algorithm. The optimal weight matrices obtained during the training are used as seed for starting
the GA in recalling, instead starting with random weight matrix. A detailed study of the comparison of
results thus obtained with the earlier results has been done. It has been observed that for Hopfield neural
networks, recall of patterns is more successful if evolution of weight matrices is applied for training
purpose also.
Efficient steganography techniques are needed for the security of digital information over the Internet and for secret data communication. Therefore, many techniques are proposed for steganography. One of these intelligent techniques is Particle Swarm Optimization (PSO) algorithm. Recently, many modifications are made to Standard PSO (SPSO) such as Human-Based Particle Swarm Optimization (HPSO). Therefore, this paper presents image steganography using HPSO in order to find best locations in image cover to hide text secret message. Then, a comparison is done between image steganography using PSO and using HPSO. Experimental results on six (256×256) cover images and different size of secret massages, prove that the performance of the proposed image steganography using HPSO has been improved in comparison with using SPSO.
Scheduling Using Multi Objective Genetic Algorithmiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Proposing a Scheduling Algorithm to Balance the Time and Energy Using an Impe...Editor IJCATR
Computational grids have become an appealing research area as they solve compute-intensive problems within the scientific
community and in industry. A grid computational power is aggregated from a huge set of distributed heterogeneous workers; hence, it
is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Unfortunately, current grid
schedulers suffer from the haste problem, which is the schedule inability to successfully allocate all input tasks. Accordingly, some tasks
fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously
allocated to other peers. This paper presents an imperialist competition algorithm (ICA) method to solve the grid scheduling problems.
The objective is to minimize the makespan and energy of the grid. Simulation results show that the grid scheduling problem can be
solved efficiently by the proposed method
An Effective PSO-inspired Algorithm for Workflow Scheduling IJECEIAES
The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers and storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on maximizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. This entails the need to adopt an effective scheduling algorithm in order to minimize task completion time (makespan). Although workflow scheduling has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this paper, we propose the LPSO, a novel algorithm for workflow scheduling problem that is based on the Particle Swarm Optimization method. Our proposed algorithm not only ensures a fast convergence but also prevents getting trapped in local extrema. We ran realistic scenarios using CloudSim and found that LPSO is superior to previously proposed algorithms and noticed that the deviation between the solution found by LPSO and the optimal solution is negligible.
Buffer Allocation Problem is an important research issue in manufacturing system design.
Objective of this paper is to find optimum buffer allocation for closed queuing network with
multi servers at each node. Sum of buffers in closed queuing network is constant. Attempt is
made to find optimum number of pallets required to maximize throughput of manufacturing
system which has pre specified space for allocating pallets. Expanded Mean Value Analysis is
used to evaluate the performance of closed queuing network. Particle Swarm Optimization is
used as generative technique to optimize the buffer allocation. Numerical experiments are
shown to explain effectiveness of procedure
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
MARKOV CHAIN AND ADAPTIVE PARAMETER SELECTION ON PARTICLE SWARM OPTIMIZERijsc
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic
behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in
the performance of PSO. As far as our investigation is concerned, most of the relevant researches are
based on computer simulations and few of them are based on theoretical approach. In this paper,
theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this
paper, which contains all the information needed for the future evolution. Then the memory-less property of
the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and
suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov
chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method
for parameter selection is proposed.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources.
The purpose of job scheduling in grid environment is to achieve high system throughput and minimize the execution time of applications.
The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively.
To obtain a good and efficient method to solve scheduling problems in grid, a new area of research is implemented. In this paper, a job
scheduling algorithm is proposed to assign jobs to available resources in grid environment. The proposed algorithm is based on Ant
Colony Optimization (ACO) algorithm. This algorithm is combined with one of the best scheduling algorithm, Suffrage. This paper uses
the result of Suffrage in proposed ACO algorithm. The main contribution of this work is to minimize the makespan of a given set of
jobs. The experimental results show that the proposed algorithm can lead to significant performance in grid environment.
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.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...ijaia
In this paper, an improved multimodal optimization (MMO) algorithm,calledLSEPSO,has been proposed. LSEPSO combinedElectrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then madesome modification onthem. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which usedn-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it triedto find a point which could be the alternative of particle's personal best. This methodprevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstratedthat the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer ijsc
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in the performance of PSO. As far as our investigation is concerned, most of the relevant researches are based on computer simulations and few of them are based on theoretical approach. In this paper, theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this paper, which contains all the information needed for the future evolution. Then the memory-less property of the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method for parameter selection is proposed.
This paper proposes a new methodology to
optimize trajectory of the path for multi-robots using
Improved particle swarm optimization Algorithm (IPSO) in
clutter Environment. IPSO technique is incorporated into
the multi-robot system in a dynamic framework, which will
provide robust performance, self-deterministic cooperation,
and coping with an inhospitable environment
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTIONijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are
compared on twelve constrained nonlinear test functions. Generally, the results show that Differential
Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and
robustness.
A Comparison of Particle Swarm Optimization and Differential Evolutionijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are compared on twelve constrained nonlinear test functions. Generally, the results show that Differential Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and robustness.
Reliable and accurate estimation of software has always been a matter of concern for industry and
academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all
types of datasets and environments. Since the motive of estimation model is to minimize the gap between
actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization,
Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of
COCOMO Model. The performance of these techniques has been analysed by established performance
measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR) projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
Problems in Task Scheduling in Multiprocessor Systemijtsrd
This Contemporary computer systems are multiprocessor or multicomputer machines. Their efficiency depends on good methods of administering the executed works. Fast processing of a parallel application is possible only when its parts are appropriately ordered in time and space. This calls for efficient scheduling policies in parallel computer systems. In this work deterministic problems of scheduling are considered. The classical scheduling theory assumed that the application in any moment of time is executed by only one processor. This assumption has been weakened recently, especially in the context of parallel and distributed computer systems. This monograph is devoted to problems of deterministic scheduling applications (or tasks according to the scheduling terminology) requiring more than one processor simultaneously. We name such applications multiprocessor tasks. In this work the complexity of open multiprocessor task scheduling problems has been established. Algorithms for scheduling multiprocessor tasks on parallel and dedicated processors are proposed. For a special case of applications with regular structure which allow for dividing it into parts of arbitrary size processed independently in parallel, a method of finding optimal scattering of work in a distributed computer system is proposed. The applications with such regular characteristics are called divisible tasks. The concept of a divisible task enables creation of tractable computation models in a wide class of computer architectures such as chains, stars, meshes, hypercubes, multistage networks. Divisible task method gives rise to the evaluation of computer system performance. Examples of such performance evaluation are presented. This work summarizes earlier works of the author as well as contains new original results. Mukul Varshney | Jyotsna | Abhakiran Rajpoot | Shivani Garg"Problems in Task Scheduling in Multiprocessor System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2198.pdf http://www.ijtsrd.com/computer-science/computer-architecture/2198/problems-in-task-scheduling-in-multiprocessor-system/mukul-varshney
Similar to EFFECTS OF THE DIFFERENT MIGRATION PERIODS ON PARALLEL MULTI-SWARM PSO (20)
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
2. 14 Computer Science & Information Technology (CS & IT)
Recently, a considerable literature has grown up around the theme of metaheuristic algorithms.
Particle swarm optimization (PSO) algorithm is developed by Kennedy and Eberhart in 1995 [4]
is a popular metaheuristic algorithm. It is a population-based and stochastic optimization
technique. It inspired from the social behaviours of bird flocks. Each individual in the population,
called particle, represents a potential solution. In recent years, many algorithms based on PSO
have been developed such as the comprehensive learning PSO (CLPSO) algorithm [5] and the
parallel comprehensive learning particle swarm optimization (PCLPSO) algorithm [6]. In recent
years, devising parallel models of algorithms has been a healthy field for developing more
efficient optimization procedures [14-17]. Parallelism is an approach not only to reduce the
resolution time but also to improve the quality of the provided solutions. In CLPSO, instead of
using a particle’s best information in the original PSO, all other particles’ historical best
information is used to update the particle’s velocity. Further, the global best position of
population in PSO is never used in CLPSO. With this strategy, CLPSO searches a larger area and
the probability of finding global optimum is increased. The PCLPSO algorithm based on CLPSO
has multiple swarms based on the master-slave paradigm and works cooperatively and
concurrently. Through PCLPSO, the solution quality and the global search ability are improved.
This article studies the effect of the different migration periods on PCLPSO algorithm.
This article has been organized in the following way: Section 2 is concerned with the
methodologies used for this study. Section 3 presents the experimental results and the findings of
the research. Finally, the article is concluded in Section 4.
2. MATERIALS & METHODS
2.1. PSO
Each particle in PSO represents a bird and offers a solution. Each particle has a fitness value
calculated by fitness function. Particles have velocity information and position information
updated during the optimization process. Each particle searches the food in the search area using
the velocity and position information. PSO aims to find the global optimum or a solution close to
the global optimum and therefore is launched with a random population. The particles update
their velocity and position information by using Equations (1) and (2) respectively. To update the
position of a particle, pbest of the particle and gbest of the whole population are used. pbest and
gbest are repeatedly updated during the optimization process. Thus, the global optimum or a
solution close to the global optimum is found at the end of the algorithm.
)(*2*)(*1** 21
d
i
dd
i
d
i
d
i
d
i
d
i
d
i XgbestrandcXpbestrandcVwV −+−+= (1)
d
i
d
i
d
i VXX += (2)
where d
iV and d
iX represent the velocity and the position of the dth dimension of the particle i.
The constant w is called inertia weight plays the role to balance between the global search ability
and local search ability [7]. c1 and c2 are the acceleration coefficients. rand1 and rand2 are the
two random numbers between 0 and 1. They affect the stochastic nature of the algorithm [8].
pbesti is the best position of the particle i. gbest is the best position in the entire swarm. The
inertia weight w is updated according to Equation (3) during the optimization process.
( ) ( ) Twwtwtw /* minmaxmax −−= (3)
3. Computer Science & Information Technology (CS & IT) 15
where wmax and wmin are the maximum and minimum inertia weights and usually set to 0.9 and
0.2 respectively [7]. t is the actual iteration number and T is the maximum number of iteration
cycles.
2.2. CLPSO
CLPSO based on PSO was proposed by Liang, Qin, Suganthan and Baskar [5]. PSO has some
deficiencies. For instance, if the gbest falls into a local minimum, the population can easily fall
into this local minimum. For this reason, CLPSO doesn’t use gbest. Another property of CLPSO
is that a particle uses also the pbests of all other particles. This method is called as the
comprehensive learning approach. The velocity of a particle in CLPSO is updated using Equation
(4).
)(*** )(
d
i
d
dfi
d
i
d
i
d
i XpbestrandcVwV −+= (4)
where fi = [fi(1), fi(2),…, fi(D)] is a list of the random selected particles which can be any particles
in the swarm including the particle i. They are determined by the Pc value, called as learning
probability, in Equation (5).
d
dfipbest )( indicates the pbest value of the particle which is stored in
the list fi of the particle i for the dth dimension. How a particle selects the pbests for each
dimension is explained in [5].
)(*** )(
d
i
d
dfi
d
i
d
i
d
i XpbestrandcVwV −+= (5)
CLPSO uses a parameter m, called the refreshing gap. It is used to learn from good exemplars
and to escape from local optima. The flowchart of the CLPSO algorithm is given in [5].
2.3. PCLPSO
Although PSO has many advantages, the main deficiency of PSO is the premature convergence
[8]. PCLPSO handles to overcome this deficiency like many PSO variants. The PCLPSO
algorithm based on CLPSO was proposed by Gülcü and Kodaz [6]. The solution quality is
enhanced through multiswarm and cooperation properties. Also, computational efficiency is
improved because PCLPSO runs parallel on a distributed environment.
A population is split into subpopulations. Each subpopulation represents a swarm and each
swarm independently runs PCLPSO algorithm. Thus, they seek the search area. There are two
types of swarms: master-swarm and slave swarm. In the cooperation technique, each swarm
periodically shares its own global best position with other swarms. The parallelism property is
that each swarm runs the algorithm on a different computer at the same time to achieve
computational efficiency. The topology is shown in Figure 1. Each swarm runs cooperatively and
synchronously the PCLPSO algorithm to find the global optimum. PCLPSO uses Jade
middleware framework [9] to establish the parallelism. The cluster specifications are so: windows
XP operating system, pentium i5 3.10 GHz, 2 GB memory, java se 1.7, Jade 4.2 and gigabit
ethernet. The flowchart of the PCLPSO algorithm is given in [6].
In the communication topology, there isn’t any directly communication between slave swarms as
shown in Figure 1. Migration process occurs periodically after a certain number of cycles. Each
swarm sends the own local best solution to the master in the PCLPSO’s migration process. The
master collects the local best solutions into a pool, called ElitePool. It chooses the best solution
4. 16 Computer Science & Information Technology (CS & IT)
from the ElitePool. This solution is sent to all slave swarms by the master. Thus, PCLPSO
obtains better and more robust solutions.
Figure 1. The communication topology [6]
3. EXPERIMENTAL RESULTS
The experiments performed in this section were designed to study the behaviour of PCLPSO by
varying the migration period. The migration period is an important parameter in PCLPSO and
affects the efficiency of the algorithm. This article studies the effect of the migration period on
PCLPSO algorithm.
Two unimodal and two multimodal benchmark functions which are well known to the global
optimization community and commonly used for the test of optimization algorithms are selected.
The formulas of the four functions are given in next subsection. The properties of these functions
are given in Table 1. The number of particles per swarm is 15. According to the dimensions of
functions, the experiments are split into three groups. The properties of these groups are given in
Table 2. The term FE in the table refers the maximum fitness evaluation.
The experiments are carried out on a cluster whose specifications are windows XP operating
system, pentium i5 3.10 GHz, 2 GB memory, java se 1.7, Jade 4.2 and gigabit ethernet. The
inertia weight w linearly decreases from 0.9 to 0.2 during the iterations, the acceleration
coefficient c is equal to 1.49445 and the refreshing gap m is equal to five. 30 independent tests
are carried out for each function. The results are given in next subsections.
Table 1. Type, Global Minimum, Function Value, Search and Initialization Ranges of the Benchmark
Functions
5. Computer Science & Information Technology (CS & IT) 17
Table 2. Parameters used in experiments
3.1. Functions
The functions used in the experiments are the following:
Sphere function:
∑=
=
D
i
ixxf
1
2
1 )( (6)
Rosenbrock function:
])1()(100[)( 22
1
1
1
2
2 −+−= +
−
=
∑ ii
D
i
i xxxxf (7)
Ackley function:
ex
D
x
D
xf
D
i
i
D
i
i ++
−
−−= ∑∑ ==
20)2cos(
1
exp
1
2.0exp20)(
11
2
3 π (8)
Griewank function:
1cos
4000
)(
1 1
2
4 +
−= ∑ ∏= =
D
i
D
i
ii
i
xx
xf (9)
Functions f1 and f2 are unimodal. Unimodal functions have only one optimum and no local
minima. Functions f3 and f4 are multimodal. Multimodal functions have only one optimum and
many local minima. They are treated as a difficult class of benchmark functions by researchers
because the number of local minima of the function grows exponentially as the number of its
dimension increases [10-13].
3.2. Results of the 10-D problems
Table 3 presents the mean of the function values for 10-D problems according to the different
migration periods. Table 4 presents the calculation time of the functions for 10-D problems. In
[6], the importance of the migration period is emphasized: if the information is very often
exchanged, then the solution quality may be better, but the computational efficiency deteriorates.
If the migration interval is longer, the computational efficiency is better, but the solution quality
may be worse. It is apparent from these tables that the computational efficiency is better when the
migration interval is equal to 100 as expected. But the best values of functions f1-f4 are obtained
when the migration intervals are equal to 11, 2, 6 and 1, respectively.
6. 18 Computer Science & Information Technology (CS & IT)
Table 3. The mean values for 10-D problems.
Table 4. The calculation time (ms) for 10-D problems
7. Computer Science & Information Technology (CS & IT) 19
Table 5. The mean values for 30-D problems.
Table 6. The calculation time (ms) for 30-D problems
8. 20 Computer Science & Information Technology (CS & IT)
Table 7. The mean values for 100-D problems.
Table 8. The calculation time (ms) for 100-D problems.
9. Computer Science & Information Technology (CS & IT) 21
3.3. Results of the 30-D problems
Table 5 presents the mean of the function values for 30-D problems according to the different
migration periods. The best mean values of functions f1-f4 are obtained when the migration
periods are equal to 1, 10, 11 and 12, respectively. Table 6 presents the calculation time of the
function values for 30-D problems.
3.4. Results of the 100-D problems
Table 7 presents the mean of the function values for 100-D problems according to the different
migration periods. The best mean values of functions f1-f4 are obtained when the migration
periods are equal to 11, 17, 5 and 15, respectively. Table 8 presents the calculation time of the
functions for 100-D problems.
4. CONCLUSIONS
The purpose of the current study was to determine the effect of the migration period on PCLPSO
algorithm. PCLPSO based on the master-slave paradigm has multiple swarms which work
cooperatively and concurrently on distributed computers. Each swarm runs the algorithm
independently. In the cooperation, the swarms exchange their own local best particle with each
other in every migration process. Thus, the diversity of the solutions increases through the
multiple swarms and cooperation. PCLPSO runs on a cluster. We used the well-known
benchmark functions in the experiments. In the experiments, the performance of PCLPSO is
analysed using different migration periods. This study has shown that the calculation time
decreases when the migration interval is longer. We obtained better results on some functions
when the migration period is around 10. The migration period should be tuned for different
problems. Namely, it varies with regard to the difficulty of problems. As future work, we plan to
investigate the number of particles to be exchanged between swarms on the performance of the
PCLPSO algorithm.
ACKNOWLEDGEMENTS
This research was supported by Scientific Research Projects Office of Necmettin Erbakan
University (Project No: 162518001-136).
REFERENCES
[1] M. Mezmaz, N. Melab, Y. Kessaci, Y.C. Lee, E.-G. Talbi, A.Y. Zomaya, D. Tuyttens, A parallel bi-
objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems, Journal of
Parallel and Distributed Computing, 71 (2011) 1497-1508.
[2] Z. Guo, J. Mi, P. Grant, An implicit parallel multigrid computing scheme to solve coupled thermal-
solute phase-field equations for dendrite evolution, Journal of Computational Physics, 231 (2012)
1781-1796.
[3] J. Pang, A.R. Lebeck, C. Dwyer, Modeling and simulation of a nanoscale optical computing system,
Journal of Parallel and Distributed Computing, 74 (2014) 2470-2483.
10. 22 Computer Science & Information Technology (CS & IT)
[4] J. Kennedy, R. Eberhart, Particle swarm optimization, 1995 Ieee International Conference on Neural
Networks Proceedings, Vols 1-6, (1995) 1942-1948.
[5] J.J. Liang, A.K. Qin, P.N. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer
for global optimization of multimodal functions, Ieee T Evolut Comput, 10 (2006) 281-295.
[6] Ş. Gülcü, H. Kodaz, A novel parallel multi-swarm algorithm based on comprehensive learning
particle swarm optimization, Engineering Applications of Artificial Intelligence, 45 (2015) 33-45.
[7] Y. Shi, R. Eberhart, A modified particle swarm optimizer, in: Evolutionary Computation
Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE
International Conference on, IEEE, 1998, pp. 69-73.
[8] F. Van Den Bergh, An analysis of particle swarm optimizers, in, University of Pretoria, 2006.
[9] F.L. Bellifemine, G. Caire, D. Greenwood, Developing multi-agent systems with JADE, John Wiley
& Sons, 2007.
[10] X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, Evolutionary Computation, IEEE
Transactions on, 3 (1999) 82-102.
[11] B.-Y. Qu, P.N. Suganthan, S. Das, A distance-based locally informed particle swarm model for
multimodal optimization, Evolutionary Computation, IEEE Transactions on, 17 (2013) 387-402.
[12] X. Li, Niching without niching parameters: particle swarm optimization using a ring topology,
Evolutionary Computation, IEEE Transactions on, 14 (2010) 150-169.
[13] S.C. Esquivel, C.A. Coello Coello, On the use of particle swarm optimization with multimodal
functions, in: Evolutionary Computation, 2003. CEC'03. The 2003 Congress on, IEEE, 2003, pp.
1130-1136.
[14] E. Alba, Parallel metaheuristics: a new class of algorithms, John Wiley & Sons, 2005.
[15] G.-W. Zhang, Z.-H. Zhan, K.-J. Du, Y. Lin, W.-N. Chen, J.-J. Li, J. Zhang, Parallel particle swarm
optimization using message passing interface, in: Proceedings of the 18th Asia Pacific Symposium
on Intelligent and Evolutionary Systems, Volume 1, Springer, 2015, pp. 55-64.
[16] M. Pedemonte, S. Nesmachnow, H. Cancela, A survey on parallel ant colony optimization, Applied
Soft Computing, 11 (2011) 5181-5197.
[17] E B. Li, K. Wada, Communication latency tolerant parallel algorithm for particle swarm
optimization, Parallel Computing, 37 (2011) 1-10.