M. Mahdavi

Sharif University of Technology, Tehrān, Ostan-e Tehran, Iran

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Publications (5)3.97 Total impact

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    ABSTRACT: Clustering has become an increasingly important task in modern application domains. Targeting useful and relevant information on the World Wide Web is a topical and highly complicated research area. Clustering techniques have been applied to categorize documents on Web and extracting knowledge from the Web. In this paper we propose novel clustering algorithms based on harmony search (HS) optimization method that deals with Web document clustering. By modeling clustering as an optimization problem, first, we propose a pure HS based clustering algorithm that finds near global optimal clusters within a reasonable time. Then we hybridize K-means and harmony clustering to achieve better clustering. Experimental results on five different data sets reveal that the proposed algorithms can find better clusters when compared to similar methods and the quality of clusters is comparable. Also proposed algorithms converge to the best known optimum faster than other methods.
    Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on; 06/2008
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    ABSTRACT: The advent of various real-time multimedia applications in high-speed networks creates a need for quality of service (QoS) based multicast routing. Two important QoS constraints are the bandwidth constraint and the end-to-end delay constraint. The QoS based multicast routing problem is a known NP-complete problem that depends on (1) bounded end-to-end delay and link bandwidth along the paths from the source to each destination, and (2) minimum cost of the multicast tree. In this paper we describe a new representation, called node parent index (NPI) representation, for representing trees and describe harmony operations accord to this representation. The presented algorithm is based on the harmony search (HS) algorithm and finds near-optimal solutions in polynomial time. We evaluate the performance and efficiency of the proposed method with a modified version of the bounded shortest multicast algorithm (BSMA) which is the best known deterministic heuristic algorithm to delay-constrained multicast problem. Simulation results reveal that the proposed algorithm can achieve a smaller average tree costs than modified BSMA with a much smaller running time for relatively large networks.
    Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on; 06/2008
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    ABSTRACT: This study presents a hybrid harmony search algorithm (HHSA) to solve engineering optimization problems with continuous design variables. Although the harmony search algorithm (HSA) has proven its ability of finding near global regions within a reasonable time, it is comparatively inefficient in performing local search. In this study sequential quadratic programming (SQP) is employed to speed up local search and improve precision of the HSA solutions. Moreover, an empirical study is performed in order to determine the impact of various parameters of the HSA on convergence behavior. Various benchmark engineering optimization problems are used to illustrate the effectiveness and robustness of the proposed algorithm. Numerical results reveal that the proposed hybrid algorithm, in most cases is more effective than the HSA and other meta-heuristic or deterministic methods.
    Computer Methods in Applied Mechanics and Engineering 01/2008; · 2.62 Impact Factor
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    ABSTRACT: Clustering the web documents is one of the most important approaches for mining and extracting knowledge from the web. Recently, one of the most attractive trends in clustering the high dimensional web pages has been tilt toward the learning and optimization approaches. In this paper, we propose novel hybrid harmony search (HS) based algorithms for clustering the web documents that finds a globally optimal partition of them into a specified number of clusters. By modeling clustering as an optimization problem, first, we propose a pure harmony search-based clustering algorithm that finds near global optimal clusters within a reasonable time. Then, we hybridize K-means and harmony clustering in two ways to achieve better clustering. Experimental results reveal that the proposed algorithms can find better clusters when compared to similar methods and also illustrate the robustness of the hybrid clustering algorithms.
    Applied Mathematics and Computation 01/2008; · 1.35 Impact Factor
  • M. Mahdavi, M. Fesanghary, E. Damangir
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    ABSTRACT: This paper develops an Improved harmony search (IHS) algorithm for solving optimization problems. IHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented. The IHS algorithm has been successfully applied to various benchmarking and standard engineering optimization problems. Numerical results reveal that the proposed algorithm can find better solutions when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems.
    Applied Mathematics and Computation. 01/2007;