Search results for: search system.
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 8890

Search results for: search system.

8770 Comparative Analysis of Different Page Ranking Algorithms

Authors: S. Prabha, K. Duraiswamy, J. Indhumathi

Abstract:

Search engine plays an important role in internet, to retrieve the relevant documents among the huge number of web pages. However, it retrieves more number of documents, which are all relevant to your search topics. To retrieve the most meaningful documents related to search topics, ranking algorithm is used in information retrieval technique. One of the issues in data miming is ranking the retrieved document. In information retrieval the ranking is one of the practical problems. This paper includes various Page Ranking algorithms, page segmentation algorithms and compares those algorithms used for Information Retrieval. Diverse Page Rank based algorithms like Page Rank (PR), Weighted Page Rank (WPR), Weight Page Content Rank (WPCR), Hyperlink Induced Topic Selection (HITS), Distance Rank, Eigen Rumor, Distance Rank Time Rank, Tag Rank, Relational Based Page Rank and Query Dependent Ranking algorithms are discussed and compared.

Keywords: Information Retrieval, Web Page Ranking, search engine, web mining, page segmentations.

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8769 Collaboration of Multi-Agent and Hyper-Heuristics Systems for Production Scheduling Problem

Authors: C. E. Nugraheni, L. Abednego

Abstract:

This paper introduces a framework based on the collaboration of multi agent and hyper-heuristics to find a solution of the real single machine production problem. There are many techniques used to solve this problem. Each of it has its own advantages and disadvantages. By the collaboration of multi agent system and hyper-heuristics, we can get more optimal solution. The hyper-heuristics approach operates on a search space of heuristics rather than directly on a search space of solutions. The proposed framework consists of some agents, i.e. problem agent, trainer agent, algorithm agent (GPHH, GAHH, and SAHH), optimizer agent, and solver agent. Some low level heuristics used in this paper are MRT, SPT, LPT, EDD, LDD, and MON

Keywords: Hyper-heuristics, multi-agent systems, scheduling problem.

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8768 Efficient Block Matching Algorithm for Motion Estimation

Authors: Zong Chen

Abstract:

Motion estimation is a key problem in video processing and computer vision. Optical flow motion estimation can achieve high estimation accuracy when motion vector is small. Three-step search algorithm can handle large motion vector but not very accurate. A joint algorithm was proposed in this paper to achieve high estimation accuracy disregarding whether the motion vector is small or large, and keep the computation cost much lower than full search.

Keywords: Motion estimation, Block Matching, Optical flow, Three step search.

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8767 Optimizing PID Parameters Using Harmony Search

Authors: N. Arulanand, P. Dhara

Abstract:

Optimizing the parameters in the controller plays a vital role in the control theory and its applications. Optimizing the PID parameters is finding out the best value from the feasible solutions. Finding the optimal value is an optimization problem. Inverted Pendulum is a very good platform for control engineers to verify and apply different logics in the field of control theory. It is necessary to find an optimization technique for the controller to tune the values automatically in order to minimize the error within the given bounds. In this paper, the algorithmic concepts of Harmony search (HS) and Genetic Algorithm (GA) have been analyzed for the given range of values. The experimental results show that HS performs well than GA.

Keywords: Genetic Algorithm, Harmony Search Algorithm, Inverted Pendulum, PID Controller.

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8766 Harmony Search-based K-Coverage Enhancement in Wireless Sensor Networks

Authors: Shaimaa M. Mohamed, Haitham S. Hamza, Imane A. Saroit

Abstract:

Many wireless sensor network applications require K-coverage of the monitored area. In this paper, we propose a scalable harmony search based algorithm in terms of execution time, K-Coverage Enhancement Algorithm (KCEA), it attempts to enhance initial coverage, and achieve the required K-coverage degree for a specific application efficiently. Simulation results show that the proposed algorithm achieves coverage improvement of 5.34% compared to K-Coverage Rate Deployment (K-CRD), which achieves 1.31% when deploying one additional sensor. Moreover, the proposed algorithm is more time efficient.

Keywords: Wireless Sensor Networks (WSN), Harmony Search Algorithms, K-Coverage, Mobile WSN.

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8765 Pay Per Click Attribution: Effects on Direct Search Traffic and Purchases

Authors: Toni Raurich, Joan Llonch-Andreu

Abstract:

This research focused on the relationship between Search Engine Marketing (SEM) and traditional advertising. The dominant assumption is that SEM does not help brand awareness and only does it in session as if it were the cost of manufacturing the product being sold. The study is methodologically developed using an experiment where the effects were determined to analyze the billboard effect. The research allowed the cross-linking of theoretical and empirical knowledge on digital marketing. This paper has validated that, this marketing generates retention as traditional advertising would by measuring brand awareness and its improvements. This changes the way performance and brand campaigns are distributed within marketing departments, effectively rebalancing budgets moving forward.

Keywords: Search engine marketing, click-through ratios, pay-per-click, marketing attribution.

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8764 Electroencephalography-Based Intention Recognition and Consensus Assessment during Emergency Response

Authors: Siyao Zhu, Yifang Xu

Abstract:

After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. Brain-computer interface is a promising option to overcome the limitations of tedious manual control and operation of robots in the urgent search-and-rescue tasks. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: Consensus assessment, electroencephalogram, EEG, emergency response, human-robot collaboration, intention recognition, search and rescue.

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8763 Analyzing the Relation of Community Group for Research Paper Bookmarking by Using Association Rule

Authors: P. Jomsri

Abstract:

Currently searching through internet is very popular especially in a field of academic. A huge of educational information such as research papers are overload for user. So community-base web sites have been developed to help user search information more easily from process of customizing a web site to need each specifies user or set of user. In this paper propose to use association rule analyze the community group on research paper bookmarking. A set of design goals for community group frameworks is developed and discussed. Additionally Researcher analyzes the initial relation by using association rule discovery between the antecedent and the consequent of a rule in the groups of user for generate the idea to improve ranking search result and development recommender system.

Keywords: association rule, information retrieval, research paper bookmarking.

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8762 A New Heuristic Algorithm for the Classical Symmetric Traveling Salesman Problem

Authors: S. B. Liu, K. M. Ng, H. L. Ong

Abstract:

This paper presents a new heuristic algorithm for the classical symmetric traveling salesman problem (TSP). The idea of the algorithm is to cut a TSP tour into overlapped blocks and then each block is improved separately. It is conjectured that the chance of improving a good solution by moving a node to a position far away from its original one is small. By doing intensive search in each block, it is possible to further improve a TSP tour that cannot be improved by other local search methods. To test the performance of the proposed algorithm, computational experiments are carried out based on benchmark problem instances. The computational results show that algorithm proposed in this paper is efficient for solving the TSPs.

Keywords: Local search, overlapped neighborhood, travelingsalesman problem.

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8761 A Tabu Search Heuristic for Scratch-Pad Memory Management

Authors: Maha Idrissi Aouad, Rene Schott, Olivier Zendra

Abstract:

Reducing energy consumption of embedded systems requires careful memory management. It has been shown that Scratch- Pad Memories (SPMs) are low size, low cost, efficient (i.e. energy saving) data structures directly managed at the software level. In this paper, the focus is on heuristic methods for SPMs management. A method is efficient if the number of accesses to SPM is as large as possible and if all available space (i.e. bits) is used. A Tabu Search (TS) approach for memory management is proposed which is, to the best of our knowledge, a new original alternative to the best known existing heuristic (BEH). In fact, experimentations performed on benchmarks show that the Tabu Search method is as efficient as BEH (in terms of energy consumption) but BEH requires a sorting which can be computationally expensive for a large amount of data. TS is easy to implement and since no sorting is necessary, unlike BEH, the corresponding sorting time is saved. In addition to that, in a dynamic perspective where the maximum capacity of the SPM is not known in advance, the TS heuristic will perform better than BEH.

Keywords: Energy consumption, memory allocation management, optimization, tabu search heuristic.

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8760 A Hybrid Particle Swarm Optimization Solution to Ramping Rate Constrained Dynamic Economic Dispatch

Authors: Pichet Sriyanyong

Abstract:

This paper presents the application of an enhanced Particle Swarm Optimization (EPSO) combined with Gaussian Mutation (GM) for solving the Dynamic Economic Dispatch (DED) problem considering the operating constraints of generators. The EPSO consists of the standard PSO and a modified heuristic search approaches. Namely, the ability of the traditional PSO is enhanced by applying the modified heuristic search approach to prevent the solutions from violating the constraints. In addition, Gaussian Mutation is aimed at increasing the diversity of global search, whilst it also prevents being trapped in suboptimal points during search. To illustrate its efficiency and effectiveness, the developed EPSO-GM approach is tested on the 3-unit and 10-unit 24-hour systems considering valve-point effect. From the experimental results, it can be concluded that the proposed EPSO-GM provides, the accurate solution, the efficiency, and the feature of robust computation compared with other algorithms under consideration.

Keywords: Particle Swarm Optimization (PSO), GaussianMutation (GM), Dynamic Economic Dispatch (DED).

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8759 Leveraging Quality Metrics in Voting Model Based Thread Retrieval

Authors: Atefeh Heydari, Mohammadali Tavakoli, Zuriati Ismail, Naomie Salim

Abstract:

Seeking and sharing knowledge on online forums have made them popular in recent years. Although online forums are valuable sources of information, due to variety of sources of messages, retrieving reliable threads with high quality content is an issue. Majority of the existing information retrieval systems ignore the quality of retrieved documents, particularly, in the field of thread retrieval. In this research, we present an approach that employs various quality features in order to investigate the quality of retrieved threads. Different aspects of content quality, including completeness, comprehensiveness, and politeness, are assessed using these features, which lead to finding not only textual, but also conceptual relevant threads for a user query within a forum. To analyse the influence of the features, we used an adopted version of voting model thread search as a retrieval system. We equipped it with each feature solely and also various combinations of features in turn during multiple runs. The results show that incorporating the quality features enhances the effectiveness of the utilised retrieval system significantly.

Keywords: Content quality, Forum search, Thread retrieval, Voting techniques.

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8758 Web portal As A Knowledge Management System In The Universities

Authors: Marjan Mansourvar, Norizan Mohd Yasin

Abstract:

The development of Web has affected different aspects of our lives, such as communication, sharing knowledge, searching for jobs, social activities, etc. The web portal as a gateway in the World Wide Web is a starting point for people who are connecting to the Internet. The web portal as the type of knowledge management system provides a rich space to share and search information as well as communication services like free email or content provision for the users. This research aims to discover the university needs to the web portal as a necessary tool for students in the universities to help them in getting the required information. A survey was conducted to gather students' requirements which can be incorporated in to portal to be developed.

Keywords: Knowledge, Knowledge management system, Knowledge sharing, web portal.

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8757 Elitist Self-Adaptive Step-Size Search in Optimum Sizing of Steel Structures

Authors: Oğuzhan Hasançebi, Saeid Kazemzadeh Azad

Abstract:

This paper covers application of an elitist selfadaptive
step-size search (ESASS) to optimum design of steel
skeletal structures. In the ESASS two approaches are considered for
improving the convergence accuracy as well as the computational
efficiency of the original technique namely the so called selfadaptive
step-size search (SASS). Firstly, an additional randomness
is incorporated into the sampling step of the technique to preserve
exploration capability of the algorithm during the optimization.
Moreover, an adaptive sampling scheme is introduced to improve the
quality of final solutions. Secondly, computational efficiency of the
technique is accelerated via avoiding unnecessary analyses during the
optimization process using an upper bound strategy. The numerical
results demonstrate the usefulness of the ESASS in the sizing
optimization problems of steel truss and frame structures.

Keywords: Structural design optimization, optimal sizing, metaheuristics, self-adaptive step-size search, steel trusses, steel frames.}

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8756 Design and Implementation of Optimal Winner Determination Algorithm in Combinatorial e- Auctions

Authors: S. Khanpour, A. Movaghar

Abstract:

The one of best robust search technique on large scale search area is heuristic and meta heuristic approaches. Especially in issue that the exploitation of combinatorial status in the large scale search area prevents the solution of the problem via classical calculating methods, so such problems is NP-complete. in this research, the problem of winner determination in combinatorial auctions have been formulated and by assessing older heuristic functions, we solve the problem by using of genetic algorithm and would show that this new method would result in better performance in comparison to other heuristic function such as simulated annealing greedy approach.

Keywords: Bids, genetic algorithm, heuristic, metaheuristic, simulated annealing greedy.

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8755 Web Content Mining: A Solution to Consumer's Product Hunt

Authors: Syed Salman Ahmed, Zahid Halim, Rauf Baig, Shariq Bashir

Abstract:

With the rapid growth in business size, today's businesses orient towards electronic technologies. Amazon.com and e-bay.com are some of the major stakeholders in this regard. Unfortunately the enormous size and hugely unstructured data on the web, even for a single commodity, has become a cause of ambiguity for consumers. Extracting valuable information from such an everincreasing data is an extremely tedious task and is fast becoming critical towards the success of businesses. Web content mining can play a major role in solving these issues. It involves using efficient algorithmic techniques to search and retrieve the desired information from a seemingly impossible to search unstructured data on the Internet. Application of web content mining can be very encouraging in the areas of Customer Relations Modeling, billing records, logistics investigations, product cataloguing and quality management. In this paper we present a review of some very interesting, efficient yet implementable techniques from the field of web content mining and study their impact in the area specific to business user needs focusing both on the customer as well as the producer. The techniques we would be reviewing include, mining by developing a knowledge-base repository of the domain, iterative refinement of user queries for personalized search, using a graphbased approach for the development of a web-crawler and filtering information for personalized search using website captions. These techniques have been analyzed and compared on the basis of their execution time and relevance of the result they produced against a particular search.

Keywords: Data mining, web mining, search engines, knowledge discovery.

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8754 Semi-Automatic Analyzer to Detect Authorial Intentions in Scientific Documents

Authors: Kanso Hassan, Elhore Ali, Soule-dupuy Chantal, Tazi Said

Abstract:

Information Retrieval has the objective of studying models and the realization of systems allowing a user to find the relevant documents adapted to his need of information. The information search is a problem which remains difficult because the difficulty in the representing and to treat the natural languages such as polysemia. Intentional Structures promise to be a new paradigm to extend the existing documents structures and to enhance the different phases of documents process such as creation, editing, search and retrieval. The intention recognition of the author-s of texts can reduce the largeness of this problem. In this article, we present intentions recognition system is based on a semi-automatic method of extraction the intentional information starting from a corpus of text. This system is also able to update the ontology of intentions for the enrichment of the knowledge base containing all possible intentions of a domain. This approach uses the construction of a semi-formal ontology which considered as the conceptualization of the intentional information contained in a text. An experiments on scientific publications in the field of computer science was considered to validate this approach.

Keywords: Information research, text analyzes, intentionalstructure, segmentation, ontology, natural language processing.

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8753 A Search Algorithm for Solving the Economic Lot Scheduling Problem with Reworks under the Basic Period Approach

Authors: Yu-Jen Chang, Shih-Chieh Chen, Yu-Wei Kuo

Abstract:

In this study, we are interested in the economic lot scheduling problem (ELSP) that considers manufacturing of the serviceable products and remanufacturing of the reworked products. In this paper, we formulate a mathematical model for the ELSP with reworks using the basic period approach. In order to solve this problem, we propose a search algorithm to find the cyclic multiplier ki of each product that can be cyclically produced for every ki basic periods. This research also uses two heuristics to search for the optimal production sequence of all lots and the optimal time length of the basic period so as to minimize the average total cost. This research uses a numerical example to show the effectiveness of our approach.

Keywords: Economic lot, reworks, inventory, basic period.

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8752 A Study and Implementation of On-line Learning Diagnosis and Inquiry System

Authors: YuLung Wu

Abstract:

In Knowledge Structure Graph, each course unit represents a phase of learning activities. Both learning portfolios and Knowledge Structure Graphs contain learning information of students and let teachers know which content are difficulties and fails. The study purposes "Dual Mode On-line Learning Diagnosis System" that integrates two search methods: learning portfolio and knowledge structure. Teachers can operate the proposed system and obtain the information of specific students without any computer science background. The teachers can find out failed students in advance and provide remedial learning resources.

Keywords: Knowledge Structure Graph, On-line LearningDiagnosis

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8751 K-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors

Authors: Shao-Tzu Huang, Chen-Chien Hsu, Wei-Yen Wang

Abstract:

Matching high dimensional features between images is computationally expensive for exhaustive search approaches in computer vision. Although the dimension of the feature can be degraded by simplifying the prior knowledge of homography, matching accuracy may degrade as a tradeoff. In this paper, we present a feature matching method based on k-means algorithm that reduces the matching cost and matches the features between images instead of using a simplified geometric assumption. Experimental results show that the proposed method outperforms the previous linear exhaustive search approaches in terms of the inlier ratio of matched pairs.

Keywords: Feature matching, k-means clustering, scale invariant feature transform, linear exhaustive search.

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8750 Parallel 2-Opt Local Search on GPU

Authors: Wen-Bao Qiao, Jean-Charles Créput

Abstract:

To accelerate the solution for large scale traveling salesman problems (TSP), a parallel 2-opt local search algorithm with simple implementation based on Graphics Processing Unit (GPU) is presented and tested in this paper. The parallel scheme is based on technique of data decomposition by dynamically assigning multiple K processors on the integral tour to treat K edges’ 2-opt local optimization simultaneously on independent sub-tours, where K can be user-defined or have a function relationship with input size N. We implement this algorithm with doubly linked list on GPU. The implementation only requires O(N) memory. We compare this parallel 2-opt local optimization against sequential exhaustive 2-opt search along integral tour on TSP instances from TSPLIB with more than 10000 cities.

Keywords: Doubly linked list, parallel 2-opt, tour division, GPU.

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8749 Predicting the Minimum Free Energy RNA Secondary Structures using Harmony Search Algorithm

Authors: Abdulqader M. Mohsen, Ahamad Tajudin Khader, Dhanesh Ramachandram, Abdullatif Ghallab

Abstract:

The physical methods for RNA secondary structure prediction are time consuming and expensive, thus methods for computational prediction will be a proper alternative. Various algorithms have been used for RNA structure prediction including dynamic programming and metaheuristic algorithms. Musician's behaviorinspired harmony search is a recently developed metaheuristic algorithm which has been successful in a wide variety of complex optimization problems. This paper proposes a harmony search algorithm (HSRNAFold) to find RNA secondary structure with minimum free energy and similar to the native structure. HSRNAFold is compared with dynamic programming benchmark mfold and metaheuristic algorithms (RnaPredict, SetPSO and HelixPSO). The results showed that HSRNAFold is comparable to mfold and better than metaheuristics in finding the minimum free energies and the number of correct base pairs.

Keywords: Metaheuristic algorithms, dynamic programming algorithms, harmony search optimization, RNA folding, Minimum free energy.

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8748 Solving Facility Location Problem on Cluster Computing

Authors: Ei Phyo Wai, Nay Min Tun

Abstract:

Computation of facility location problem for every location in the country is not easy simultaneously. Solving the problem is described by using cluster computing. A technique is to design parallel algorithm by using local search with single swap method in order to solve that problem on clusters. Parallel implementation is done by the use of portable parallel programming, Message Passing Interface (MPI), on Microsoft Windows Compute Cluster. In this paper, it presents the algorithm that used local search with single swap method and implementation of the system of a facility to be opened by using MPI on cluster. If large datasets are considered, the process of calculating a reasonable cost for a facility becomes time consuming. The result shows parallel computation of facility location problem on cluster speedups and scales well as problem size increases.

Keywords: cluster, cost, demand, facility location

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8747 An Effective Hybrid Genetic Algorithm for Job Shop Scheduling Problem

Authors: Bin Cai, Shilong Wang, Haibo Hu

Abstract:

The job shop scheduling problem (JSSP) is well known as one of the most difficult combinatorial optimization problems. This paper presents a hybrid genetic algorithm for the JSSP with the objective of minimizing makespan. The efficiency of the genetic algorithm is enhanced by integrating it with a local search method. The chromosome representation of the problem is based on operations. Schedules are constructed using a procedure that generates full active schedules. In each generation, a local search heuristic based on Nowicki and Smutnicki-s neighborhood is applied to improve the solutions. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

Keywords: Genetic algorithm, Job shop scheduling problem, Local search, Meta-heuristic algorithm

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8746 An Hybrid Approach for Loss Reduction in Distribution Systems using Harmony Search Algorithm

Authors: R. Srinivasa Rao

Abstract:

Individually Network reconfiguration or Capacitor control perform well in minimizing power loss and improving voltage profile of the distribution system. But for heavy reactive power loads network reconfiguration and for heavy active power loads capacitor placement can not effectively reduce power loss and enhance voltage profiles in the system. In this paper, an hybrid approach that combine network reconfiguration and capacitor placement using Harmony Search Algorithm (HSA) is proposed to minimize power loss reduction and improve voltage profile. The proposed approach is tested on standard IEEE 33 and 16 bus systems. Computational results show that the proposed hybrid approach can minimize losses more efficiently than Network reconfiguration or Capacitor control. The results of proposed method are also compared with results obtained by Simulated Annealing (SA). The proposed method has outperformed in terms of the quality of solution compared to SA.

Keywords: Capacitor Control, Network Reconfiguration, HarmonySearch Algorithm, Loss Reduction, Voltage Profile.

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8745 Automatic Generation Control of Multi-Area Electric Energy Systems Using Modified GA

Authors: Gayadhar Panda, Sidhartha Panda, C. Ardil

Abstract:

A modified Genetic Algorithm (GA) based optimal selection of parameters for Automatic Generation Control (AGC) of multi-area electric energy systems is proposed in this paper. Simulations on multi-area reheat thermal system with and without consideration of nonlinearity like governor dead band followed by 1% step load perturbation is performed to exemplify the optimum parameter search. In this proposed method, a modified Genetic Algorithm is proposed where one point crossover with modification is employed. Positional dependency in respect of crossing site helps to maintain diversity of search point as well as exploitation of already known optimum value. This makes a trade-off between exploration and exploitation of search space to find global optimum in less number of generations. The proposed GA along with decomposition technique as developed has been used to obtain the optimum megawatt frequency control of multi-area electric energy systems. Time-domain simulations are conducted with trapezoidal integration along with decomposition technique. The superiority of the proposed method over existing one is verified from simulations and comparisons.

Keywords: Automatic Generation Control (AGC), Reheat, Proportional Integral (PI) controller, Dead Band, Genetic Algorithm(GA).

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8744 A Low-Cost Vision-Based Unmanned Aerial System for Extremely Low-Light GPS-Denied Navigation and Thermal Imaging

Authors: Chang Liu, John Nash, Stephen D. Prior

Abstract:

This paper presents the design and implementation details of a complete unmanned aerial system (UAS) based on commercial-off-the-shelf (COTS) components, focusing on safety, security, search and rescue scenarios in GPS-denied environments. In particular, The aerial platform is capable of semi-autonomously navigating through extremely low-light, GPS-denied indoor environments based on onboard sensors only, including a downward-facing optical flow camera. Besides, an additional low-cost payload camera system is developed to stream both infra-red video and visible light video to a ground station in real-time, for the purpose of detecting sign of life and hidden humans. The total cost of the complete system is estimated to be $1150, and the effectiveness of the system has been tested and validated in practical scenarios.

Keywords: Unmanned aerial system, commercial-off-the-shelf, extremely low-light, GPS-denied, optical flow, infrared video.

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8743 A General Variable Neighborhood Search Algorithm to Minimize Makespan of the Distributed Permutation Flowshop Scheduling Problem

Authors: G. M. Komaki, S. Mobin, E. Teymourian, S. Sheikh

Abstract:

This paper addresses minimizing the makespan of the distributed permutation flow shop scheduling problem. In this problem, there are several parallel identical factories or flowshops each with series of similar machines. Each job should be allocated to one of the factories and all of the operations of the jobs should be performed in the allocated factory. This problem has recently gained attention and due to NP-Hard nature of the problem, metaheuristic algorithms have been proposed to tackle it. Majority of the proposed algorithms require large computational time which is the main drawback. In this study, a general variable neighborhood search algorithm (GVNS) is proposed where several time-saving schemes have been incorporated into it. Also, the GVNS uses the sophisticated method to change the shaking procedure or perturbation depending on the progress of the incumbent solution to prevent stagnation of the search. The performance of the proposed algorithm is compared to the state-of-the-art algorithms based on standard benchmark instances.

Keywords: Distributed permutation flow shop, scheduling, makespan, general variable neighborhood search algorithm.

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8742 Optimizing of Fuzzy C-Means Clustering Algorithm Using GA

Authors: Mohanad Alata, Mohammad Molhim, Abdullah Ramini

Abstract:

Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Consequently, the main objective of this paper is to use the subtractive clustering algorithm to provide the optimal number of clusters needed by FCM algorithm by optimizing the parameters of the subtractive clustering algorithm by an iterative search approach and then to find an optimal weighting exponent (m) for the FCM algorithm. In order to get an optimal number of clusters, the iterative search approach is used to find the optimal single-output Sugenotype Fuzzy Inference System (FIS) model by optimizing the parameters of the subtractive clustering algorithm that give minimum least square error between the actual data and the Sugeno fuzzy model. Once the number of clusters is optimized, then two approaches are proposed to optimize the weighting exponent (m) in the FCM algorithm, namely, the iterative search approach and the genetic algorithms. The above mentioned approach is tested on the generated data from the original function and optimal fuzzy models are obtained with minimum error between the real data and the obtained fuzzy models.

Keywords: Fuzzy clustering, Fuzzy C-Means, Genetic Algorithm, Sugeno fuzzy systems.

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8741 A hybrid Tabu Search Algorithm to Cell Formation Problem and its Variants

Authors: Tai-Hsi Wu, Jinn-Yi Yeh, Chin-Chih Chang

Abstract:

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

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