Search results for: Great Deluge Algorithm.
4253 Fuzzy Population-Based Meta-Heuristic Approaches for Attribute Reduction in Rough Set Theory
Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi
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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.Keywords: Rough Set Theory, Attribute Reduction, Fuzzy Logic, Memetic Algorithms, Record to Record Algorithm, Great Deluge Algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19274252 A Genetic Based Algorithm to Generate Random Simple Polygons Using a New Polygon Merge Algorithm
Authors: Ali Nourollah, Mohsen Movahedinejad
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In this paper a new algorithm to generate random simple polygons from a given set of points in a two dimensional plane is designed. The proposed algorithm uses a genetic algorithm to generate polygons with few vertices. A new merge algorithm is presented which converts any two polygons into a simple polygon. This algorithm at first changes two polygons into a polygonal chain and then the polygonal chain is converted into a simple polygon. The process of converting a polygonal chain into a simple polygon is based on the removal of intersecting edges. The experiments results show that the proposed algorithm has the ability to generate a great number of different simple polygons and has better performance in comparison to celebrated algorithms such as space partitioning and steady growth.
Keywords: Divide and conquer, genetic algorithm, merge polygons, Random simple polygon generation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32794251 Fast Codevector Search Algorithm for 3-D Vector Quantized Codebook
Authors: H. B. Kekre, Tanuja K. Sarode
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This paper presents a very simple and efficient algorithm for codebook search, which reduces a great deal of computation as compared to the full codebook search. The algorithm is based on sorting and centroid technique for search. The results table shows the effectiveness of the proposed algorithm in terms of computational complexity. In this paper we also introduce a new performance parameter named as Average fractional change in pixel value as we feel that it gives better understanding of the closeness of the image since it is related to the perception. This new performance parameter takes into consideration the average fractional change in each pixel value.Keywords: Vector Quantization, Data Compression, Encoding, Searching.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16034250 Metaheuristic Algorithms for Decoding Binary Linear Codes
Authors: Hassan Berbia, Faissal Elbouanani, Rahal Romadi, Mostafa Belkasmi
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This paper introduces two decoders for binary linear codes based on Metaheuristics. The first one uses a genetic algorithm and the second is based on a combination genetic algorithm with a feed forward neural network. The decoder based on the genetic algorithms (DAG) applied to BCH and convolutional codes give good performances compared to Chase-2 and Viterbi algorithm respectively and reach the performances of the OSD-3 for some Residue Quadratic (RQ) codes. This algorithm is less complex for linear block codes of large block length; furthermore their performances can be improved by tuning the decoder-s parameters, in particular the number of individuals by population and the number of generations. In the second algorithm, the search space, in contrast to DAG which was limited to the code word space, now covers the whole binary vector space. It tries to elude a great number of coding operations by using a neural network. This reduces greatly the complexity of the decoder while maintaining comparable performances.Keywords: Block code, decoding, methaheuristic, genetic algorithm, neural network
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20714249 A hybrid Tabu Search Algorithm to Cell Formation Problem and its Variants
Authors: Tai-Hsi Wu, Jinn-Yi Yeh, Chin-Chih Chang
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Cell formation is the first step in the design of cellular manufacturing systems. In this study, a general purpose computational scheme employing a hybrid tabu search algorithm as the core is proposed to solve the cell formation problem and its variants. In the proposed scheme, great flexibilities are left to the users. The core solution searching algorithm embedded in the scheme can be easily changed to any other meta-heuristic algorithms, such as the simulated annealing, genetic algorithm, etc., based on the characteristics of the problems to be solved or the preferences the users might have. In addition, several counters are designed to control the timing of conducting intensified solution searching and diversified solution searching strategies interactively.Keywords: Cell formation problem, Tabu search
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17284248 A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem
Authors: Gaohuizi Guo, Ning Zhang
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Firefly algorithm (FA) and Sine Cosine algorithm (SCA) are two very popular and advanced metaheuristic algorithms. However, these algorithms applied to multi-objective optimization problems have some shortcomings, respectively, such as premature convergence and limited exploration capability. Combining the privileges of FA and SCA while avoiding their deficiencies may improve the accuracy and efficiency of the algorithm. This paper proposes a hybridization of FA and SCA algorithms, named multi-objective firefly-sine cosine algorithm (MFA-SCA), to develop a more efficient meta-heuristic algorithm than FA and SCA.Keywords: Firefly algorithm, hybrid algorithm, multi-objective optimization, Sine Cosine algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4944247 Investigation on Bio-Inspired Population Based Metaheuristic Algorithms for Optimization Problems in Ad Hoc Networks
Authors: C. Rajan, K. Geetha, C. Rasi Priya, R. Sasikala
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Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution. Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algorithms leads to the efficient transmission of data in mobile ad hoc networks. The algorithms that are inspired by the principles of naturally-distributed/collective behavior of social colonies have shown excellence in dealing with complex optimization problems. Thus some of the bio-inspired metaheuristic algorithms help to increase the efficiency of routing in ad hoc networks. This survey work presents the overview of bio-inspired metaheuristic algorithms which support the efficiency of routing in mobile ad hoc networks.
Keywords: Ant colony optimization algorithm, Genetic algorithm, naturally inspired algorithms and particle swarm optimization algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 36004246 Approximating Fixed Points by a Two-Step Iterative Algorithm
Authors: Safeer Hussain Khan
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In this paper, we introduce a two-step iterative algorithm to prove a strong convergence result for approximating common fixed points of three contractive-like operators. Our algorithm basically generalizes an existing algorithm..Our iterative algorithm also contains two famous iterative algorithms: Mann iterative algorithm and Ishikawa iterative algorithm. Thus our result generalizes the corresponding results proved for the above three iterative algorithms to a class of more general operators. At the end, we remark that nothing prevents us to extend our result to the case of the iterative algorithm with error terms.
Keywords: Contractive-like operator, iterative algorithm, fixed point, strong convergence.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20104245 On the Computation of a Common n-finger Robotic Grasp for a Set of Objects
Authors: Avishai Sintov, Roland Menassa, Amir Shapiro
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Industrial robotic arms utilize multiple end-effectors, each for a specific part and for a specific task. We propose a novel algorithm which will define a single end-effector’s configuration able to grasp a given set of objects with different geometries. The algorithm will have great benefit in production lines allowing a single robot to grasp various parts. Hence, reducing the number of endeffectors needed. Moreover, the algorithm will reduce end-effector design and manufacturing time and final product cost. The algorithm searches for a common grasp over the set of objects. The search algorithm maps all possible grasps for each object which satisfy a quality criterion and takes into account possible external wrenches (forces and torques) applied to the object. The mapped grasps are- represented by high-dimensional feature vectors which describes the shape of the gripper. We generate a database of all possible grasps for each object in the feature space. Then we use a search and classification algorithm for intersecting all possible grasps over all parts and finding a single common grasp suitable for all objects. We present simulations of planar and spatial objects to validate the feasibility of the approach.
Keywords: Common Grasping, Search Algorithm, Robotic End-Effector.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16634244 Some Improvements on Kumlander-s Maximum Weight Clique Extraction Algorithm
Authors: Satoshi Shimizu, Kazuaki Yamaguchi, Toshiki Saitoh, Sumio Masuda
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Some fast exact algorithms for the maximum weight clique problem have been proposed. Östergard’s algorithm is one of them. Kumlander says his algorithm is faster than it. But we confirmed that the straightforwardly implemented Kumlander’s algorithm is slower than O¨ sterga˚rd’s algorithm. We propose some improvements on Kumlander’s algorithm.
Keywords: Maximum weight clique, exact algorithm, branch-andbound, NP-hard.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18474243 Application of Adaptive Genetic Algorithm in Function Optimization
Authors: Panpan Xu, Shulin Sui
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The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.
Keywords: Genetic algorithm, Adaptive genetic algorithm, Function optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17094242 Optimal External Merge Sorting Algorithm with Smart Block Merging
Authors: Mir Hadi Seyedafsari, Iraj Hasanzadeh
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Like other external sorting algorithms, the presented algorithm is a two step algorithm including internal and external steps. The first part of the algorithm is like the other similar algorithms but second part of that is including a new easy implementing method which has reduced the vast number of inputoutput operations saliently. As decreasing processor operating time does not have any effect on main algorithm speed, any improvement in it should be done through decreasing the number of input-output operations. This paper propose an easy algorithm for choose the correct record location of the final list. This decreases the time complexity and makes the algorithm faster.Keywords: External sorting algorithm, internal sortingalgorithm, fast sorting, robust algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21704241 Analog Circuit Design using Genetic Algorithm: Modified
Authors: Amod P. Vaze
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Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to analog circuit design automation. These researches show a better performance due to the nature of Genetic Algorithm. In this paper a modified Genetic Algorithm is applied for analog circuit design automation. The modifications are made to the topology of the circuit. These modifications will lead to a more computationally efficient algorithm.
Keywords: Genetic algorithm, analog circuits, design.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22824240 Application of Hybrid Genetic Algorithm Based on Simulated Annealing in Function Optimization
Authors: Panpan Xu, Shulin Sui, Zongjie Du
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Genetic algorithm is widely used in optimization problems for its excellent global search capabilities and highly parallel processing capabilities; but, it converges prematurely and has a poor local optimization capability in actual operation. Simulated annealing algorithm can avoid the search process falling into local optimum. A hybrid genetic algorithm based on simulated annealing is designed by combining the advantages of genetic algorithm and simulated annealing algorithm. The numerical experiment represents the hybrid genetic algorithm can be applied to solve the function optimization problems efficiently.Keywords: Genetic algorithm, Simulated annealing, Hybrid genetic algorithm, Function optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25404239 Convergence Analysis of an Alternative Gradient Algorithm for Non-Negative Matrix Factorization
Authors: Chenxue Yang, Mao Ye, Zijian Liu, Tao Li, Jiao Bao
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Non-negative matrix factorization (NMF) is a useful computational method to find basis information of multivariate nonnegative data. A popular approach to solve the NMF problem is the multiplicative update (MU) algorithm. But, it has some defects. So the columnwisely alternating gradient (cAG) algorithm was proposed. In this paper, we analyze convergence of the cAG algorithm and show advantages over the MU algorithm. The stability of the equilibrium point is used to prove the convergence of the cAG algorithm. A classic model is used to obtain the equilibrium point and the invariant sets are constructed to guarantee the integrity of the stability. Finally, the convergence conditions of the cAG algorithm are obtained, which help reducing the evaluation time and is confirmed in the experiments. By using the same method, the MU algorithm has zero divisor and is convergent at zero has been verified. In addition, the convergence conditions of the MU algorithm at zero are similar to that of the cAG algorithm at non-zero. However, it is meaningless to discuss the convergence at zero, which is not always the result that we want for NMF. Thus, we theoretically illustrate the advantages of the cAG algorithm.
Keywords: Non-negative matrix factorizations, convergence, cAG algorithm, equilibrium point, stability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16884238 Genetic Mining: Using Genetic Algorithm for Topic based on Concept Distribution
Authors: S. M. Khalessizadeh, R. Zaefarian, S.H. Nasseri, E. Ardil
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Today, Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to text classification, summarization and information retrieval system in text mining process. This researches show a better performance due to the nature of Genetic Algorithm. In this paper a new algorithm for using Genetic Algorithm in concept weighting and topic identification, based on concept standard deviation will be explored.Keywords: Genetic Algorithm, Text Mining, Term Weighting, Concept Extraction, Concept Distribution.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 36934237 A Novel Methodology Proposed for Optimizing the Degree of Hybridization in Parallel HEVs using Genetic Algorithm
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In this paper, a new Genetic Algorithm (GA) based methodology is proposed to optimize the Degree of Hybridization (DOH) in a passenger parallel hybrid car. At first step, target parameters for the vehicle are decided and then using ADvanced VehIcle SimulatOR (ADVISOR) software, the variation pattern of these target parameters, across the different DOHs, is extracted. At the next step, a suitable cost function is defined and is optimized using GA. In this paper, also a new technique has been proposed for deciding the number of battery modules for each DOH, which leads to a great improvement in the vehicle performance. The proposed methodology is so simple, fast and at the same time, so efficient.Keywords: Degree of Hybridization (DOH), Electric Motor, Emissions, Fuel Economy, Genetic Algorithm (GA), Hybrid ElectricVehicle (HEV), Vehicle Performance
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18354236 Application of ESA in the CAVE Mode Authentication
Authors: Keonwoo Kim, Dowon Hong, Kyoil Chung
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This paper proposes the authentication method using ESA algorithm instead of using CAVE algorithm in the CDMA mobile communication systems including IS-95 and CDMA2000 1x. And, we analyze to apply ESA mechanism on behalf of CAVE mechanism without the change of message format and air interface in the existing CDMA systems. If ESA algorithm can be used as the substitution of CAVE algorithm, security strength of authentication algorithm is intensified without protocol change. An algorithm replacement proposed in this paper is not to change an authentication mechanism, but to configure input of ESA algorithm and to produce output. Therefore, our proposal can be the compatible to the existing systems.Keywords: ESA, CAVE, CDMA, authentication, mobilecommunication.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15774235 A New Algorithm to Stereo Correspondence Using Rank Transform and Morphology Based On Genetic Algorithm
Authors: Razagh Hafezi, Ahmad Keshavarz, Vida Moshfegh
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This paper presents a novel algorithm of stereo correspondence with rank transform. In this algorithm we used the genetic algorithm to achieve the accurate disparity map. Genetic algorithms are efficient search methods based on principles of population genetic, i.e. mating, chromosome crossover, gene mutation, and natural selection. Finally morphology is employed to remove the errors and discontinuities.Keywords: genetic algorithm, morphology, rank transform, stereo correspondence
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21584234 A Constrained Clustering Algorithm for the Classification of Industrial Ores
Authors: Luciano Nieddu, Giuseppe Manfredi
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In this paper a Pattern Recognition algorithm based on a constrained version of the k-means clustering algorithm will be presented. The proposed algorithm is a non parametric supervised statistical pattern recognition algorithm, i.e. it works under very mild assumptions on the dataset. The performance of the algorithm will be tested, togheter with a feature extraction technique that captures the information on the closed two-dimensional contour of an image, on images of industrial mineral ores.Keywords: K-means, Industrial ores classification, Invariant Features, Supervised Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13714233 An Innovative Fuzzy Decision Making Based Genetic Algorithm
Authors: M. A. Sharbafi, M. Shakiba Herfeh, Caro Lucas, A. Mohammadi Nejad
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Several researchers have proposed methods about combination of Genetic Algorithm (GA) and Fuzzy Logic (the use of GA to obtain fuzzy rules and application of fuzzy logic in optimization of GA). In this paper, we suggest a new method in which fuzzy decision making is used to improve the performance of genetic algorithm. In the suggested method, we determine the alleles that enhance the fitness of chromosomes and try to insert them to the next generation. In this algorithm we try to present an innovative vaccination in the process of reproduction in genetic algorithm, with considering the trade off between exploration and exploitation.Keywords: Genetic Algorithm, Fuzzy Decision Making.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15914232 Enhanced Imperialist Competitive Algorithm for the Cell Formation Problem Using Sequence Data
Authors: S. H. Borghei, E. Teymourian, M. Mobin, G. M. Komaki, S. Sheikh
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Imperialist Competitive Algorithm (ICA) is a recent meta-heuristic method that is inspired by the social evolutions for solving NP-Hard problems. The ICA is a population-based algorithm which has achieved a great performance in comparison to other metaheuristics. This study is about developing enhanced ICA approach to solve the Cell Formation Problem (CFP) using sequence data. In addition to the conventional ICA, an enhanced version of ICA, namely EICA, applies local search techniques to add more intensification aptitude and embed the features of exploration and intensification more successfully. Suitable performance measures are used to compare the proposed algorithms with some other powerful solution approaches in the literature. In the same way, for checking the proficiency of algorithms, forty test problems are presented. Five benchmark problems have sequence data, and other ones are based on 0-1 matrices modified to sequence based problems. Computational results elucidate the efficiency of the EICA in solving CFP problems.Keywords: Cell formation problem, Group technology, Imperialist competitive algorithm, Sequence data.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15804231 FILMS based ANC System – Evaluation and Practical Implementation
Authors: Branislav Vuksanović, Dragana Nikolić
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This paper describes the implementation and testing of a multichannel active noise control system (ANCS) based on the filtered-inverse LMS (FILMS) algorithm. The FILMS algorithm is derived from the well-known filtered-x LMS (FXLMS) algorithm with the aim to improve the rate of convergence of the multichannel FXLMS algorithm and to reduce its computational load. Laboratory setup and techniques used to implement this system efficiently are described in this paper. Experiments performed in order to test the performance of the FILMS algorithm are discussed and the obtained results presented.Keywords: Active noise control, adaptive filters, inverse filters, LMS algorithm, FILMS algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16214230 Simulation of Tracking Time Delay Algorithm using Mathcad Package
Authors: Mahmud Hesain ALdwaik, Omar Hsiain Eldwaik
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This paper deals with tracking and estimating time delay between two signals. The simulation of this algorithm accomplished by using Mathcad package is carried out. The algorithm we will present adaptively controls and tracking the delay, so as to minimize the mean square of this error. Thus the algorithm in this case has task not only of seeking the minimum point of error but also of tracking the change of position, leading to a significant improving of performance. The flowchart of the algorithm is presented as well as several tests of different cases are carried out.
Keywords: Tracking time delay, Algorithm simulation, Mathcad, MSE
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20434229 A New Algorithm for Cluster Initialization
Authors: Moth'd Belal. Al-Daoud
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Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the k-means algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximum variance. The algorithm has been applied to different data sets and good results are obtained.
Keywords: clustering, k-means, data mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20954228 The Research of Fuzzy Classification Rules Applied to CRM
Authors: Chien-Hua Wang, Meng-Ying Chou, Chin-Tzong Pang
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In the era of great competition, understanding and satisfying customers- requirements are the critical tasks for a company to make a profits. Customer relationship management (CRM) thus becomes an important business issue at present. With the help of the data mining techniques, the manager can explore and analyze from a large quantity of data to discover meaningful patterns and rules. Among all methods, well-known association rule is most commonly seen. This paper is based on Apriori algorithm and uses genetic algorithms combining a data mining method to discover fuzzy classification rules. The mined results can be applied in CRM to help decision marker make correct business decisions for marketing strategies.Keywords: Customer relationship management (CRM), Data mining, Apriori algorithm, Genetic algorithm, Fuzzy classification rules.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16444227 Improving the Performance of Back-Propagation Training Algorithm by Using ANN
Authors: Vishnu Pratap Singh Kirar
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Artificial Neural Network (ANN) can be trained using back propagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a twoterm algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.
Keywords: Neural Network, Backpropagation, Local Minima, Fast Convergence Rate.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35464226 A New Evolutionary Algorithm for Cluster Analysis
Authors: B.Bahmani Firouzi, T. Niknam, M. Nayeripour
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Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.
Keywords: Data clustering, Hybrid evolutionary optimization algorithm, K-means algorithm, Simulated Annealing (SA), Particle Swarm Optimization (PSO).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22684225 Scheduling Multiple Workflow Using De-De Dodging Algorithm and PBD Algorithm in Cloud: Detailed Study
Authors: B. Arun Kumar, T. Ravichandran
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Workflow scheduling is an important part of cloud computing and based on different criteria it decides cost, execution time, and performances. A cloud workflow system is a platform service facilitating automation of distributed applications based on new cloud infrastructure. An aspect which differentiates cloud workflow system from others is market-oriented business model, an innovation which challenges conventional workflow scheduling strategies. Time and Cost optimization algorithm for scheduling Hybrid Clouds (TCHC) algorithm decides which resource should be chartered from public providers is combined with a new De-De algorithm considering that every instance of single and multiple workflows work without deadlocks. To offset this, two new concepts - De-De Dodging Algorithm and Priority Based Decisive Algorithm - combine with conventional deadlock avoidance issues by proposing one algorithm that maximizes active (not just allocated) resource use and reduces Makespan.Keywords: Workflow Scheduling, cloud workflow, TCHC algorithm, De-De Dodging Algorithm, Priority Based Decisive Algorithm (PBD), Makespan.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27914224 Adaptive Fuzzy Control on EDF Scheduling
Authors: Xiangbin Zhu
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EDF (Early Deadline First) algorithm is a very important scheduling algorithm for real- time systems . The EDF algorithm assigns priorities to each job according to their absolute deadlines and has good performance when the real-time system is not overloaded. When the real-time system is overloaded, many misdeadlines will be produced. But these misdeadlines are not uniformly distributed, which usually focus on some tasks. In this paper, we present an adaptive fuzzy control scheduling based on EDF algorithm. The improved algorithm can have a rectangular distribution of misdeadline ratios among all real-time tasks when the system is overloaded. To evaluate the effectiveness of the improved algorithm, we have done extensive simulation studies. The simulation results show that the new algorithm is superior to the old algorithm.
Keywords: Fuzzy control, real-time systems, EDF, misdeadline ratio.
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