This document proposes and evaluates a new metaheuristic optimization algorithm called Current Search (CS) and applies it to optimize PID controller parameters for DC motor speed control. The CS is inspired by electric current flow and aims to balance exploration and exploitation. It outperforms genetic algorithm, particle swarm optimization, and adaptive tabu search on benchmark optimization problems, finding better solutions faster. When applied to optimize a PID controller for DC motor speed control, the CS successfully controlled motor speed.
The document presents an optimization algorithm called System Rank Ordering Heuristic (System RO-H) for queries with conjunction of predicates. System RO-H extends the traditional System R optimization algorithm by:
1. Using a heuristic called h-metric to order predicates for joining relations.
2. The h-metric orders predicates in ascending order based on either the predicate's rank or a ratio of selectivity and cost per tuple, whichever is lower.
3. By ordering predicates based on h-metric, System RO-H finds optimal plans in both left-deep and bushy join trees in polynomial time relative to the number of predicates.
This document discusses a hybridization of the Magnetic Charge System Search (MCSS) method for efficient data clustering. MCSS is a meta-heuristic algorithm inspired by electromagnetic theory that has shown potential but also has issues with convergence rate and getting stuck in local optima. The authors propose a Hybrid MCSS (HMCSS) that incorporates a local search strategy and differential evolution inspired updating to improve convergence. An experiment on benchmark functions and real clustering problems shows HMCSS provides better results than existing algorithms and enhances MCSS convergence.
The document discusses implementing an integrated approach of the K-means clustering algorithm for prediction analysis. It begins with motivating the need to improve the accuracy and dependability of existing overlapping K-means clustering by removing its dependency on random initialization parameters. The proposed methodology determines the optimal number of clusters K based on the dataset, calculates initial centroid positions using a harmonic means method, and applies overlapping K-means clustering. The implementation and results on two large datasets show the integrated approach outperforms original overlapping K-means in terms of accuracy, F-measure, Rand index, and number of iterations.
A HYBRID CLUSTERING ALGORITHM FOR DATA MININGcscpconf
The document proposes a hybrid clustering algorithm that combines K-means and K-harmonic mean algorithms. It performs clustering by alternating between using harmonic mean and arithmetic mean to recalculate cluster centers after each iteration. Experimental results on five datasets show the hybrid algorithm produces clusters with lower mean values, indicating tighter grouping, compared to traditional K-means and K-harmonic mean algorithms. The hybrid approach overcomes issues with initialization sensitivity and helps improve computation time and clustering accuracy.
The document analyzes crop yield data from spatial locations in Guntur District, Andhra Pradesh, India using hybrid data mining techniques. It first applies k-means clustering to the dataset, producing 5 clusters. It then applies the J48 classification algorithm to the clustered data, resulting in a decision tree that predicts cluster membership based on attributes like crop type, irrigated area, and latitude. Analysis found irrigated areas of cotton and chilies increased from 2007-2008 to 2011-2012. Association rule mining on the clustered data also found relationships between productivity and location attributes. The hybrid approach of clustering followed by classification effectively analyzed the spatial agricultural data.
Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Opti...Xin-She Yang
This document discusses applying an eagle strategy inspired by nature to engineering optimization problems. The eagle strategy uses a two-stage approach combining global exploration with local exploitation. Global exploration uses Lèvy flights for random walks to diversify solutions. Promising solutions are then locally optimized using an efficient local search algorithm like particle swarm optimization. The document analyzes random walk models like Lèvy flights and how they can maintain diversity in swarm intelligence algorithms. It applies the eagle strategy to four engineering design problems, finding Lèvy flights can effectively reduce computational efforts.
HYBRID GENETIC ALGORITHM FOR BI-CRITERIA MULTIPROCESSOR TASK SCHEDULING WITH ...aciijournal
Present work considers the minimization of the bi-criteria function including weighted sum of makespan and total completion time for a Multiprocessor task scheduling problem.Genetic algorithm is the most
appealing choice for the different NP hard problems including multiprocessor task scheduling.
Performance of genetic algorithm depends on the quality of initial solution as good initial solution provides the better results. Different list scheduling heuristics based hybrid genetic algorithms (HGAs) have been
proposed and developedfor the problem. Computational analysis with the help of defined performance
index has been conducted on the standard task scheduling problems for evaluating the performance of the
proposed HGAs. The analysis shows that the ETF-GA is quite efficient and best among the other heuristic based hybrid genetic algorithms in terms of solution quality especially for large and complex problems.
The document presents an optimization algorithm called System Rank Ordering Heuristic (System RO-H) for queries with conjunction of predicates. System RO-H extends the traditional System R optimization algorithm by:
1. Using a heuristic called h-metric to order predicates for joining relations.
2. The h-metric orders predicates in ascending order based on either the predicate's rank or a ratio of selectivity and cost per tuple, whichever is lower.
3. By ordering predicates based on h-metric, System RO-H finds optimal plans in both left-deep and bushy join trees in polynomial time relative to the number of predicates.
This document discusses a hybridization of the Magnetic Charge System Search (MCSS) method for efficient data clustering. MCSS is a meta-heuristic algorithm inspired by electromagnetic theory that has shown potential but also has issues with convergence rate and getting stuck in local optima. The authors propose a Hybrid MCSS (HMCSS) that incorporates a local search strategy and differential evolution inspired updating to improve convergence. An experiment on benchmark functions and real clustering problems shows HMCSS provides better results than existing algorithms and enhances MCSS convergence.
The document discusses implementing an integrated approach of the K-means clustering algorithm for prediction analysis. It begins with motivating the need to improve the accuracy and dependability of existing overlapping K-means clustering by removing its dependency on random initialization parameters. The proposed methodology determines the optimal number of clusters K based on the dataset, calculates initial centroid positions using a harmonic means method, and applies overlapping K-means clustering. The implementation and results on two large datasets show the integrated approach outperforms original overlapping K-means in terms of accuracy, F-measure, Rand index, and number of iterations.
A HYBRID CLUSTERING ALGORITHM FOR DATA MININGcscpconf
The document proposes a hybrid clustering algorithm that combines K-means and K-harmonic mean algorithms. It performs clustering by alternating between using harmonic mean and arithmetic mean to recalculate cluster centers after each iteration. Experimental results on five datasets show the hybrid algorithm produces clusters with lower mean values, indicating tighter grouping, compared to traditional K-means and K-harmonic mean algorithms. The hybrid approach overcomes issues with initialization sensitivity and helps improve computation time and clustering accuracy.
The document analyzes crop yield data from spatial locations in Guntur District, Andhra Pradesh, India using hybrid data mining techniques. It first applies k-means clustering to the dataset, producing 5 clusters. It then applies the J48 classification algorithm to the clustered data, resulting in a decision tree that predicts cluster membership based on attributes like crop type, irrigated area, and latitude. Analysis found irrigated areas of cotton and chilies increased from 2007-2008 to 2011-2012. Association rule mining on the clustered data also found relationships between productivity and location attributes. The hybrid approach of clustering followed by classification effectively analyzed the spatial agricultural data.
Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Opti...Xin-She Yang
This document discusses applying an eagle strategy inspired by nature to engineering optimization problems. The eagle strategy uses a two-stage approach combining global exploration with local exploitation. Global exploration uses Lèvy flights for random walks to diversify solutions. Promising solutions are then locally optimized using an efficient local search algorithm like particle swarm optimization. The document analyzes random walk models like Lèvy flights and how they can maintain diversity in swarm intelligence algorithms. It applies the eagle strategy to four engineering design problems, finding Lèvy flights can effectively reduce computational efforts.
HYBRID GENETIC ALGORITHM FOR BI-CRITERIA MULTIPROCESSOR TASK SCHEDULING WITH ...aciijournal
Present work considers the minimization of the bi-criteria function including weighted sum of makespan and total completion time for a Multiprocessor task scheduling problem.Genetic algorithm is the most
appealing choice for the different NP hard problems including multiprocessor task scheduling.
Performance of genetic algorithm depends on the quality of initial solution as good initial solution provides the better results. Different list scheduling heuristics based hybrid genetic algorithms (HGAs) have been
proposed and developedfor the problem. Computational analysis with the help of defined performance
index has been conducted on the standard task scheduling problems for evaluating the performance of the
proposed HGAs. The analysis shows that the ETF-GA is quite efficient and best among the other heuristic based hybrid genetic algorithms in terms of solution quality especially for large and complex problems.
A Non Parametric Estimation Based Underwater Target ClassifierCSCJournals
Underwater noise sources constitute a prominent class of input signal in most underwater signal processing systems. The problem of identification of noise sources in the ocean is of great importance because of its numerous practical applications. In this paper, a methodology is presented for the detection and identification of underwater targets and noise sources based on non parametric indicators. The proposed system utilizes Cepstral coefficient analysis and the Kruskal-Wallis H statistic along with other statistical indicators like F-test statistic for the effective detection and classification of noise sources in the ocean. Simulation results for typical underwater noise data and the set of identified underwater targets are also presented in this paper.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Urban strategies to promote resilient cities The case of enhancing Historic C...inventionjournals
This research tackles disaster prevention problems in dense urban areas, concentrating on the urban fire challenge in Historic Cairo district, Egypt, through disaster risk management approach. The study area suffers from the strike of several urban fire outbreaks, that resulted in disfiguring historic monuments and destroying unregulated traditional markets. Therefore, the study investigates the significance of hazard management and how can urban strategies improve the city resilient through reducing the impact of natural and man-made threats. The main findings of the research are the determination of the vulnerability factors in Historic Cairo district, either regarding management deficiency or issues related to the existing urban form. It is found that the absence of the mitigation and preparedness phases is the main problem in the risk management cycle in the case study. Additionally, the coping initiatives adopted by local authorities to address risks are random and insufficient. The study concludes with recommendations which invoke incorporating hazard management stages (pre disaster, during disaster and post disaster) into the process of evolving development planning. Finally, solutions are offered to mitigate, prepare, respond and recover from fire disasters in the case study. The solutions include urban policies, land-use planning, urban design outlines, safety regulation and public awareness and training.
Sca a sine cosine algorithm for solving optimization problemslaxmanLaxman03209
The document proposes a new population-based optimization algorithm called the Sine Cosine Algorithm (SCA) for solving optimization problems. SCA creates multiple random initial solutions and uses sine and cosine functions to fluctuate the solutions outward or toward the best solution, emphasizing exploration and exploitation. The performance of SCA is evaluated on test functions, qualitative metrics, and by optimizing the cross-section of an aircraft wing, showing it can effectively explore, avoid local optima, converge to the global optimum, and solve real problems with constraints.
A survey on optimal route queries for road networkseSAT Journals
Abstract
In daily life the need to find optimal routes between two points is critical, for example finding the shortest distance to the nearest
hospital. Internet based maps are now widely used for this purpose. Route search and optimal route queries are two important classes
of queries based on road network concept. Route search queries find the route according to the given constraints. The optimal route
queries find the optimum route from a set of specifications by a user. In road map queries, users have to give the specification of
starting point and ending point of their travelling with or without constraints. Some spatial features about the categories and the
different locations should be specified along with this. If the travelling constraints are given then it should be unique. These
constraints may be either total order or partial order. In this specification order there should be information about both starting point
and destination point of the travelling. The optimal route queries optimize the possible routes and give the optimal route that satisfies
all the constraints. This paper describes the survey on optimal route query processing, two categories namely optimal route query
processing and spatial search with categorical information have been considered, a discussion on technique for optimal route query
with constraints and without constraint is also included. The total order needs a specification of list of points and in the same order
that they should be visited but that is not required for partial order constraints. Finally this paper concludes with pros and cons of
different techniques under optimal route queries.
Keywords: Query processing, optimal route queries, Spatial search, Categorical information, Constraints.
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
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, 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 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.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
1. The document describes a heuristic approach for solving the cluster traveling salesman problem (CTSP) using genetic algorithms.
2. The proposed algorithm divides nodes into pre-specified clusters, uses GA to find a Hamiltonian path for each cluster, then combines the optimized cluster paths to form a full tour.
3. The algorithm was tested on symmetric TSPLIB instances and shown to find high quality solutions faster than two other metaheuristic approaches for CTSP.
Survey on classification algorithms for data mining (comparison and evaluation)Alexander Decker
This document provides an overview and comparison of three classification algorithms: K-Nearest Neighbors (KNN), Decision Trees, and Bayesian Networks. It discusses each algorithm, including how KNN classifies data based on its k nearest neighbors. Decision Trees classify data based on a tree structure of decisions, and Bayesian Networks classify data based on probabilities of relationships between variables. The document conducts an analysis of these three algorithms to determine which has the best performance and lowest time complexity for classification tasks based on evaluating a mock dataset over 24 months.
ENHANCING COMPUTATIONAL EFFORTS WITH CONSIDERATION OF PROBABILISTIC AVAILABL...Raja Larik
This document proposes a Probabilistic Collocation Method (PCM) to improve probabilistic load flow (PLF) computation methods and model network topology uncertainties. PCM uses probability distribution functions to model the impact of uncertainties as a linear function of power injections. It maintains the linear relationship between line flows and power injections. The method is examined using the IEEE 39-bus test system and compared to Monte Carlo simulation, showing significantly reduced computational efforts while maintaining accuracy.
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.
IRJET- Customer Segmentation from Massive Customer Transaction DataIRJET Journal
This document discusses various methods for customer segmentation through analysis of massive customer transaction data, including K-Means clustering, PAM clustering, agglomerative clustering, divisive clustering, and density-based clustering. It finds that K-Means is the most commonly used partitioning method. The document also reviews related work on customer segmentation and clustering algorithms like CLARA, CLARANS, BIRCH, ROCK, CHAMELEON, CURE, DHCC, DBSCAN, and LOF. It proposes a framework for an online shopping site that would apply these techniques to group customers based on their product preferences in transaction data.
A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systemsijtsrd
Firefly Algorithm (FA) is a newly proposed computation technique with inherent parallelism, capable for local as well as global search, meta-heuristic and robust in computing process. In this paper, Firefly Algorithm for Dynamic System (FADS) is a proposed system to find instantaneous behavior of the dynamic system within a single framework based on the idealized behavior of the flashing characteristics of fireflies. Dynamic system where flows of mass and / or energy is cause of dynamicity is generally represented as a set of differential equations and Fourth Order Runge-Kutta (RK4) method is one of used tool for numerical measurement of instantaneous behaviours of dynamic system. In FADS, experimental results are demonstrating the existence of more accurate and effective RK4 technique for the study of dynamic system. Gautam Mahapatra | Srijita Mahapatra | Soumya Banerjee"A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8393.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/8393/a-study-of-firefly-algorithm-and-its-application-in-non-linear-dynamic-systems/gautam-mahapatra
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.
Este documento trata sobre circuitos magnéticos. Explica los fundamentos de los transformadores y máquinas eléctricas. El autor es Dr. Lionel R. Orama Exclusa y es la clase 8 de esta serie sobre circuitos magnéticos.
This document provides an introduction to three-phase circuits and power. It defines key concepts like real power, reactive power, and power factor for sinusoidal voltages and currents. It describes how to calculate real and reactive power from rms voltage, current, and phase angle. Balanced three-phase systems are introduced, and how they allow more efficient power transmission compared to single-phase systems. Equations for solving problems involving three-phase circuits are also presented.
A Non Parametric Estimation Based Underwater Target ClassifierCSCJournals
Underwater noise sources constitute a prominent class of input signal in most underwater signal processing systems. The problem of identification of noise sources in the ocean is of great importance because of its numerous practical applications. In this paper, a methodology is presented for the detection and identification of underwater targets and noise sources based on non parametric indicators. The proposed system utilizes Cepstral coefficient analysis and the Kruskal-Wallis H statistic along with other statistical indicators like F-test statistic for the effective detection and classification of noise sources in the ocean. Simulation results for typical underwater noise data and the set of identified underwater targets are also presented in this paper.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
This summary provides the key details from the document in 3 sentences:
The document proposes a genetic algorithm approach combined with a local search algorithm inspired by binary gravitational attraction to solve scheduling problems in grid computing. The algorithm aims to minimize task completion time and costs by optimizing resource selection and load balancing. Experimental results showed that the proposed algorithm achieved better optimization of time and costs and selection of resources compared to other algorithms.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Urban strategies to promote resilient cities The case of enhancing Historic C...inventionjournals
This research tackles disaster prevention problems in dense urban areas, concentrating on the urban fire challenge in Historic Cairo district, Egypt, through disaster risk management approach. The study area suffers from the strike of several urban fire outbreaks, that resulted in disfiguring historic monuments and destroying unregulated traditional markets. Therefore, the study investigates the significance of hazard management and how can urban strategies improve the city resilient through reducing the impact of natural and man-made threats. The main findings of the research are the determination of the vulnerability factors in Historic Cairo district, either regarding management deficiency or issues related to the existing urban form. It is found that the absence of the mitigation and preparedness phases is the main problem in the risk management cycle in the case study. Additionally, the coping initiatives adopted by local authorities to address risks are random and insufficient. The study concludes with recommendations which invoke incorporating hazard management stages (pre disaster, during disaster and post disaster) into the process of evolving development planning. Finally, solutions are offered to mitigate, prepare, respond and recover from fire disasters in the case study. The solutions include urban policies, land-use planning, urban design outlines, safety regulation and public awareness and training.
Sca a sine cosine algorithm for solving optimization problemslaxmanLaxman03209
The document proposes a new population-based optimization algorithm called the Sine Cosine Algorithm (SCA) for solving optimization problems. SCA creates multiple random initial solutions and uses sine and cosine functions to fluctuate the solutions outward or toward the best solution, emphasizing exploration and exploitation. The performance of SCA is evaluated on test functions, qualitative metrics, and by optimizing the cross-section of an aircraft wing, showing it can effectively explore, avoid local optima, converge to the global optimum, and solve real problems with constraints.
A survey on optimal route queries for road networkseSAT Journals
Abstract
In daily life the need to find optimal routes between two points is critical, for example finding the shortest distance to the nearest
hospital. Internet based maps are now widely used for this purpose. Route search and optimal route queries are two important classes
of queries based on road network concept. Route search queries find the route according to the given constraints. The optimal route
queries find the optimum route from a set of specifications by a user. In road map queries, users have to give the specification of
starting point and ending point of their travelling with or without constraints. Some spatial features about the categories and the
different locations should be specified along with this. If the travelling constraints are given then it should be unique. These
constraints may be either total order or partial order. In this specification order there should be information about both starting point
and destination point of the travelling. The optimal route queries optimize the possible routes and give the optimal route that satisfies
all the constraints. This paper describes the survey on optimal route query processing, two categories namely optimal route query
processing and spatial search with categorical information have been considered, a discussion on technique for optimal route query
with constraints and without constraint is also included. The total order needs a specification of list of points and in the same order
that they should be visited but that is not required for partial order constraints. Finally this paper concludes with pros and cons of
different techniques under optimal route queries.
Keywords: Query processing, optimal route queries, Spatial search, Categorical information, Constraints.
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
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, 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 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.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
1. The document describes a heuristic approach for solving the cluster traveling salesman problem (CTSP) using genetic algorithms.
2. The proposed algorithm divides nodes into pre-specified clusters, uses GA to find a Hamiltonian path for each cluster, then combines the optimized cluster paths to form a full tour.
3. The algorithm was tested on symmetric TSPLIB instances and shown to find high quality solutions faster than two other metaheuristic approaches for CTSP.
Survey on classification algorithms for data mining (comparison and evaluation)Alexander Decker
This document provides an overview and comparison of three classification algorithms: K-Nearest Neighbors (KNN), Decision Trees, and Bayesian Networks. It discusses each algorithm, including how KNN classifies data based on its k nearest neighbors. Decision Trees classify data based on a tree structure of decisions, and Bayesian Networks classify data based on probabilities of relationships between variables. The document conducts an analysis of these three algorithms to determine which has the best performance and lowest time complexity for classification tasks based on evaluating a mock dataset over 24 months.
ENHANCING COMPUTATIONAL EFFORTS WITH CONSIDERATION OF PROBABILISTIC AVAILABL...Raja Larik
This document proposes a Probabilistic Collocation Method (PCM) to improve probabilistic load flow (PLF) computation methods and model network topology uncertainties. PCM uses probability distribution functions to model the impact of uncertainties as a linear function of power injections. It maintains the linear relationship between line flows and power injections. The method is examined using the IEEE 39-bus test system and compared to Monte Carlo simulation, showing significantly reduced computational efforts while maintaining accuracy.
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.
IRJET- Customer Segmentation from Massive Customer Transaction DataIRJET Journal
This document discusses various methods for customer segmentation through analysis of massive customer transaction data, including K-Means clustering, PAM clustering, agglomerative clustering, divisive clustering, and density-based clustering. It finds that K-Means is the most commonly used partitioning method. The document also reviews related work on customer segmentation and clustering algorithms like CLARA, CLARANS, BIRCH, ROCK, CHAMELEON, CURE, DHCC, DBSCAN, and LOF. It proposes a framework for an online shopping site that would apply these techniques to group customers based on their product preferences in transaction data.
A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systemsijtsrd
Firefly Algorithm (FA) is a newly proposed computation technique with inherent parallelism, capable for local as well as global search, meta-heuristic and robust in computing process. In this paper, Firefly Algorithm for Dynamic System (FADS) is a proposed system to find instantaneous behavior of the dynamic system within a single framework based on the idealized behavior of the flashing characteristics of fireflies. Dynamic system where flows of mass and / or energy is cause of dynamicity is generally represented as a set of differential equations and Fourth Order Runge-Kutta (RK4) method is one of used tool for numerical measurement of instantaneous behaviours of dynamic system. In FADS, experimental results are demonstrating the existence of more accurate and effective RK4 technique for the study of dynamic system. Gautam Mahapatra | Srijita Mahapatra | Soumya Banerjee"A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8393.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/8393/a-study-of-firefly-algorithm-and-its-application-in-non-linear-dynamic-systems/gautam-mahapatra
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.
Este documento trata sobre circuitos magnéticos. Explica los fundamentos de los transformadores y máquinas eléctricas. El autor es Dr. Lionel R. Orama Exclusa y es la clase 8 de esta serie sobre circuitos magnéticos.
This document provides an introduction to three-phase circuits and power. It defines key concepts like real power, reactive power, and power factor for sinusoidal voltages and currents. It describes how to calculate real and reactive power from rms voltage, current, and phase angle. Balanced three-phase systems are introduced, and how they allow more efficient power transmission compared to single-phase systems. Equations for solving problems involving three-phase circuits are also presented.
Crafting effective messages for environmenal conservationCaren Magill-Myers
This document discusses how to craft effective environmental conservation messages using social norms. It reviews literature showing that normative social influence, implying that others are adopting pro-environmental behaviors, is actually a highly effective motivator despite being perceived as less influential. Two studies are described that demonstrate the power of normative messages: a survey found normative beliefs correlated with conservation efforts, and an experiment found normative door hangers reduced energy use more than other messages. The key finding is that messages merely stating what the majority does can significantly increase pro-environmental behaviors.
This document discusses fundamentals of transformers and electric machines. It includes figures and MATLAB code showing sinusoidal voltage and current waveforms, with the current waveform delayed or advanced in phase relative to the voltage. The document is a class lecture on the topic presented by Dr. Lionel R. Orama and includes copyright notices on each page.
HTML merupakan bahasa standar untuk menampilkan dokumen web. Dokumen HTML terdiri dari tag-tag seperti <html>, <head>, <body> yang mendefinisikan struktur dasar dokumen. Tag-tag lain seperti <p>, <h1>-<h6> digunakan untuk paragraf dan judul, sedangkan <br> dan <hr> digunakan untuk line break dan garis horizontal.
Identification of parameters of an induction motor from field solution 9 11 n...Valentino Selayan
This document describes a method for identifying the equivalent circuit parameters of induction motors from numerical field solutions. The method uses a 2D harmonic finite element model coupled with external circuit elements to account for 3D effects. Parameter values are predicted by simulating no-load and locked rotor tests on the FE model. The accuracy of the predicted parameters is evaluated by applying the method to two test motors and comparing the results to experimental data, finding good agreement within 5% error for most parameters. The method provides a fast and accurate way to predict motor performance at the design stage using numerical field solutions.
What are the work attributes and background macrotrends affecting job growth in the \'sustainability\' field? A presentaiton delivered at the ORC West Coast Meeting, Sep 2009, Dana Point, CA
This study explores how a decision support system (DSS) can help reduce confirmation bias through a "de-biasing" function that presents counter-arguments. The researchers designed an experiment to test if exposure to computer-mediated counter-arguments through a DSS can facilitate individual learning and improve decision satisfaction by helping users reform their mental models. The results suggest that the de-biasing function was effective in increasing learning and decreasing confidence levels compared to those not exposed to counter-arguments.
Bab lima membahas proses pengumpulan data kuantitatif yang terdiri dari 5 langkah utama yaitu menentukan sampel, memperoleh perizinan, menentukan jenis data yang dikumpulkan, memilih instrumen, dan mengadministrasikan pengumpulan data. Proses ini melibatkan pemilihan sampel berdasarkan probabilitas atau nonprobabilitas, penggunaan berbagai jenis instrumen untuk mengukur variabel, serta pertimbangan validitas dan reliabilitas
The document is an agenda for a presentation on estimating the value of natural resources. It will include an overview of ecological economics, the global ecological footprint, methods for estimating ecosystem value, and an group activity to evaluate estimation methods with an example. It also discusses definitions and perceptions of sustainability, challenges related to population growth and resource use, and objectives of ecological economics around sustainable scale, efficient allocation, and just distribution.
Modul ini membahas tentang komponen-komponen utama yang dibutuhkan untuk merakit sebuah komputer pribadi (PC), mulai dari perangkat proses seperti processor dan motherboard, perangkat display seperti graphics card dan monitor, perangkat penyimpanan seperti hard disk dan optical drive, serta komponen-komponen pendukung lainnya. Modul ini juga menjelaskan fungsi dan karakteristik dari masing-masing komponen tersebut.
The document contains lecture notes about transformers. It discusses that a transformer transfers electrical energy from one voltage level to another without mechanical energy conversion. It has two circuits called primary and secondary linked by a magnetic circuit. An AC voltage applied to the primary induces a voltage in the secondary according to Faraday's law of induction. The core is made of laminated steel to reduce eddy current losses. Transformers can be of core type or shell type and are used to step up or step down voltages for applications like power distribution or electronics.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Harmony Search Algorithm Based Optimal Placement of Static Capacitors for Los...IRJET Journal
This document proposes using a Harmony Search Algorithm (HSA) to optimally place static capacitors in a radial distribution network in order to reduce power losses. HSA is a metaheuristic optimization technique inspired by musical improvisation that can handle both continuous and discrete variables. It is applied to determine the best locations and sizes of capacitors at different load levels on the IEEE 69-bus test system. The results show that optimal capacitor placement using HSA reduces power losses and improves the voltage profile compared to other techniques like genetic algorithms.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...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 imperialist competition algorithm (ICA) and gravitational attraction is used for to address the
problem of independent task scheduling in a grid environment, with the aim of reducing the makespan and energy. Experimental results
compare ICA with other algorithms and illustrate that ICA finds a shorter makespan and energy relative to the others. Moreover, it
converges quickly, finding its optimum solution in less time than the other algorithms.
A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
Particle Swarm Optimization to Solve Multiple Traveling Salesman ProblemIRJET Journal
This document proposes a new genetic ant colony optimization algorithm for solving the multiple traveling salesman problem (mTSP). The algorithm combines properties of genetic algorithms and ant colony optimization. Each salesman's route is determined using ant colony optimization, while the routes of different salesmen are combined into a complete solution controlled by the genetic algorithm. The algorithm is tested on benchmark problem instances and shown to perform efficiently compared to other existing algorithms for mTSP. Key aspects of the algorithm include the representation of solutions, crossover operators that always generate feasible solutions, and the integration of ant colony optimization and genetic algorithms.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
The document summarizes research comparing the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms for optimizing power consumption using smart energy meter data. Both algorithms were implemented in MATLAB and tested on 15 days of meter data from a university lab in India. PSO achieved an 11.5% reduction in power consumption while DE achieved a 9.4% reduction. PSO outperformed DE for this application, showing it is an effective technique for optimizing energy use and reducing electricity costs for consumers. Future work could integrate the models with real smart meters and controllers to achieve automated scheduling and greater savings.
COMPARATIVE PERFORMANCE ANALYSIS OF RNSC AND MCL ALGORITHMS ON POWER-LAW DIST...acijjournal
Cluster analysis of graph related problems is an important issue now-a-day. Different types of graph
clustering techniques are appeared in the field but most of them are vulnerable in terms of effectiveness
and fragmentation of output in case of real-world applications in diverse systems. In this paper, we will
provide a comparative behavioural analysis of RNSC (Restricted Neighbourhood Search Clustering) and
MCL (Markov Clustering) algorithms on Power-Law Distribution graphs. RNSC is a graph clustering
technique using stochastic local search. RNSC algorithm tries to achieve optimal cost clustering by
assigning some cost functions to the set of clusterings of a graph. This algorithm was implemented by A.
D. King only for undirected and unweighted random graphs. Another popular graph clustering
algorithm MCL is based on stochastic flow simulation model for weighted graphs. There are plentiful
applications of power-law or scale-free graphs in nature and society. Scale-free topology is stochastic i.e.
nodes are connected in a random manner. Complex network topologies like World Wide Web, the web of
human sexual contacts, or the chemical network of a cell etc., are basically following power-law
distribution to represent different real-life systems. This paper uses real large-scale power-law
distribution graphs to conduct the performance analysis of RNSC behaviour compared with Markov
clustering (MCL) algorithm. Extensive experimental results on several synthetic and real power-law
distribution datasets reveal the effectiveness of our approach to comparative performance measure of
these algorithms on the basis of cost of clustering, cluster size, modularity index of clustering results and
normalized mutual information (NMI).
This paper proposes a method for adapting the dictionary elements in kernel-based nonlinear adaptive filtering algorithms. The dictionary contains a subset of input vectors that are used to approximate the nonlinear system. Typically, elements are added to the dictionary but never removed or adapted. The proposed method considers dictionary elements as adjustable model parameters that can be optimized to minimize the instantaneous output error, while maintaining coherence to control complexity. Gradient-based adaptation is derived for polynomial and radial basis kernels. Dictionary adaptation is incorporated into Kernel Recursive Least Squares, Kernel Normalized Least Mean Squares, and Kernel Affine Projection algorithms. Experiments on simulated and real data demonstrate that dictionary adaptation can reduce error or dictionary size compared to non-adaptive methods.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
BPSO&1-NN algorithm-based variable selection for power system stability ident...IJAEMSJORNAL
Due to the very high nonlinearity of the power system, traditional analytical methods take a lot of time to solve, causing delay in decision-making. Therefore, quickly detecting power system instability helps the control system to make timely decisions become the key factor to ensure stable operation of the power system. Power system stability identification encounters large data set size problem. The need is to select representative variables as input variables for the identifier. This paper proposes to apply wrapper method to select variables. In which, Binary Particle Swarm Optimization (BPSO) algorithm combines with K-NN (K=1) identifier to search for good set of variables. It is named BPSO&1-NN. Test results on IEEE 39-bus diagram show that the proposed method achieves the goal of reducing variables with high accuracy.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
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
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
This document summarizes a research paper that proposes a new Particle Swarm Optimization (PSO) based K-Prototype clustering algorithm to cluster mixed numeric and categorical data. It begins with background information on clustering algorithms like K-Means, K-Modes, and K-Prototype. It then describes the K-Prototype algorithm, PSO, and discrete binary PSO. Related work integrating PSO with other clustering algorithms is also reviewed. The proposed approach uses binary PSO to select improved initial prototypes for K-Prototype clustering in order to obtain better clustering results than traditional K-Prototype and avoid local optima.
This document discusses using particle swarm optimization to improve the k-prototype clustering algorithm. The k-prototype algorithm clusters data with both numeric and categorical attributes but can get stuck in local optima. The proposed method uses particle swarm optimization, a global optimization technique, to guide the k-prototype algorithm towards better clusterings. Particle swarm optimization models potential solutions as particles that explore the search space. It is integrated with k-prototype clustering to avoid locally optimal solutions and produce better clusterings. The method is tested on standard benchmark datasets and shown to outperform traditional k-modes and k-prototype clustering algorithms.
1) The document describes a vehicle routing project that uses a multi-commodity network flow formulation to explore sub-optimal solutions for object classification with noisy sensors on a 2D grid.
2) It formulates the problem as assigning tasks to vehicles (commodities) that must flow through the graph in 4 directions while being constrained by boundaries and returning to base.
3) The algorithm uses a look-ahead window to consider future moves and a rollout step using linear programming to approximate costs farther in time and decide optimal vehicle movements.
Operator himpunan fuzzy dan kaidah fuzzy digunakan untuk mengevaluasi keanggotaan beberapa karyawan ke dalam kategori umur dan tinggi tertentu. Fungsi keanggotaan umur dan tinggi digambarkan, kemudian dievaluasi apakah masing-masing karyawan termasuk kategori parobaya, tinggi, atau keduanya.
Dokumen tersebut membahas dua metode sistem fuzzy, yaitu metode Sugeno dan metode Tsukamoto. Metode Sugeno menggunakan singleton sebagai fungsi keanggotaan konsekuen sedangkan metode Tsukamoto menggunakan fungsi keanggotaan monoton. Kedua metode berbeda dalam cara defuzzyfikasinya.
Dokumen tersebut membahas tentang sistem inferensi fuzzy yang meliputi proses-prosesnya seperti fuzzyfikasi, operasi logika fuzzy, implikasi, agregasi, dan defuzzyfikasi untuk menghasilkan nilai tegas dari nilai samar sebagai masukan.
Dokumen tersebut membahas tentang pengantar logika fuzzy. Logika fuzzy pertama kali dikembangkan oleh Lotfi A. Zadeh pada tahun 1965 untuk mengatasi ketidakpastian dan ketidaktepatan dalam penalaran manusia. Logika fuzzy lebih banyak diterapkan di Jepang karena budaya Timur yang lebih menerima konsep "abu-abu". Logika fuzzy diterapkan untuk masalah yang mengandung unsur ketidakpastian dengan menggunakan konsep himp
Modul ini membahas pengenalan bahasa pemrograman C++ mulai dari struktur dasar penulisan kode program C++, tipe data dasar, perintah input, output, dan contoh praktek pembuatan program sederhana untuk menampilkan biodata.
1) Torque is produced in an induction motor due to the interaction between the rotating stator flux and currents induced in the rotor coils.
2) As the stator flux rotates, it induces sinusoidally varying voltages in three rotor coils placed 120 degrees apart, causing currents to flow in the coils.
3) The currents in the rotor coils interact with the rotating stator flux to produce a torque that tends to rotate the rotor in the same direction as the stator flux.
The induction machine, invented by Nikola Tesla in 1888, has become widely used for electromechanical energy conversion due to its ease of manufacture and robustness. It is available in power ratings from fractional horsepower to megawatts. As an AC electromechanical energy conversion device, the induction machine interfaces with the external world through both a mechanical rotating shaft port and an electrical terminal port to connect to a three-phase AC supply. This module will discuss the common three-phase induction machine.
Mata kuliah Pengantar Komputer membahas tentang sistem komputer dan elemen-elemennya selama dua semester. Tujuannya agar mahasiswa memahami konsep dasar sistem komputer dan dapat menggunakan komputer secara efektif. Materi kuliah meliputi kategori pengguna komputer, elemen sistem komputer, dan perangkat keras komputer. Mahasiswa dievaluasi melalui tugas kelompok dan kuis.
Lecture 28 360 chapter 9_ power electronics invertersValentino Selayan
This document is very short and does not contain much substantive information to summarize. It consists of only the letters "EEE" without any other context or details provided. Therefore, a meaningful 3 sentence summary cannot be generated from the limited information given.