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Working papers
Upper bounding the price of Anarchy in Atomic Splittable Selfish Routing joint work with Kamayar Khodamoradi and Mohammad Ghodsi. [PDF]
Efficient hybrid algorithms for document clustering joint work with R. Forsati, M. R. Meybodi
Weighted K-means algorithm
for Stable Clustering
Efficient approximate
algorithm for energy efficient multicast routing in MANET networks
Swap bribery
Graph Embedding for data mining algorithms
Theoretical analysis of HS algorithm behavior
Journal papers
Harmony K-means
Algorithm for Document Clustering
joint work with Hassan Abolhassani.
in Data Mining and Knowledge Discovery (Springer), To Appear. [PDF]
Fast and high quality document clustering is a crucial task in organizing information, search engine results, enhancing web crawling, and information retrieval or filtering. Recent studies have shown that the most commonly used partition-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we propose a novel Harmony K-means Algorithm (HKA) that deals with document clustering based on Harmony Search (HS) optimization method. It is proved by means of finite Markov chain theory that the HKA converges to the global optimum. To demonstrate the effectiveness and speed of HKA, we have applied HKA algorithms on some standard datasets. We also compare the HKA with other meta-heuristic and model-based document clustering approaches. Experimental results reveal that the HKA algorithm converges to the best known optimum faster than other methods and the quality of clusters are comparable.
An Improved harmony
search Algorithm for solving optimization problems
joint work with M. Fesanghary and E. Damangir.
in Appl. Math. and Comput., Volume 188, Issue 2, 15 May 2007, pp. 1567-1579. [PDF]
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.
Algorithms for
Bandwidth-delay-constrained Least-cost Multicast Routing
joint work with R. Forsati, A. T. Haghighat.
in Computer Communications, Volume 31, Issue 10, 25 June 2008, pp. 2505-2519, Elsevier. [PDF]
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 presents novel centralized algorithms to solve the bandwidth-delay-constrained least-cost multicast routing Steiner tree problem based on the Harmony Search (HS) algorithm. Our first algorithm uses modified Prüfer number as Steiner tree representation that is called HSPR. Prüfer number has poor locality and heritability in evolutionary search, so, we describe a new representation, Node Parent Index (NPI) representation, for representing trees and describe harmony operations accord to this representation. Our second algorithm is based on NPI representation that is called HSNPI, an empirical study to determine the impacts of different parameters of the HSNPI algorithm on the solution quality and convergence behavior was performed. We evaluate the performance and efficiency of our proposed methods with a GA-based algorithm and a modified version of the bounded shortest multicast algorithm (BSMA). Simulation results on randomly generated networks and real topologies indicate that HSNPI algorithm that we proposed has overcome other three algorithms on a variety of random generated networks considering average tree cost.
Novel
meta-heuristic Algorithms for Clustering Web Documents
joint work with M.H. Chehreghani, H. Abolhassani, and R. Forsati.
in Appl. Math. and Comput., Volume 201, Issues 1-2, 15 July 2008, pp. 441-451, Elsevier. [PDF]
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.
Global Best Harmony
Search
joint work with M. G. H. Omran.
in Appl. Math. Comput., (2008), doi:10.1016/j.amc.2007.09.004. [PDF]
Harmony Search (HS) is a new meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments pitches searching for a perfect state of harmony. A new variant of HS, called global-best harmony search (GHS), is proposed in this paper where concepts from swarm intelligence are borrowed to enhance the performance of HS. The performance of the GHS is investigated and compared with HS and a recently-developed variation of HS. The experiments conducted show that the GHS generally outperformed the other approaches when applied to ten benchmark problems. The effect of noise on the performance of the three HS variants is investigated and a scalability study is conducted. The effect of the GHS parameters is analyzed. Finally, the three HS variants are compared on several Integer Programming test problems. The results show that the three approaches seem to be an efficient alternative for solving Integer Programming problems.
Hybridizing
Sequential Quadratic Programming with HS Algorithm for Optimization Problems
joint work with M. Fesanghary and M. Minary, Y. Alizadeh.
in Computer Methods in Applied Mechanics and Engineering, Volume 197, Issues 33-40, 1 June 2008, pp. 3080-3091, Elsevier [PDF]
This paper presents a Hybrid Harmony Search (HHS) algorithm to solve continuous optimization problems. Although Harmony Search (HS) has proven its ability to identify the high performance regions of the solution space at a reasonable time, but it is comparatively inefficient in performing local search within those areas. This drawback can be avoided by means of local optimization algorithms. In order to obtain a high quality solution, the proposed hybrid method uses sequential quadratic programming (SQP) as a local optimizer. An empirical study to determining the impacts of different algorithm's parameters on solution evolution was performed. The effectiveness of the proposed algorithm is demonstrated through various engineering optimization problems, including mathematical function minimization and structural engineering optimization problems. Numerical results reveal that the proposed hybrid algorithm, in most cases is more effective than HS and other meta-heuristic or deterministic methods.
Conference papers
Algorithms for
Concurrency Control in Sliding Window Query Processing
joint work with R. Forsati and M. Mahmoodi
in 15th Iranian Conference on Electrical Engineering(ICEE2007) , Tehran, Iran [PDF]
Web Page
Recommendation Based on Distributed Learning Automata and PageRank Algorithm
joint work with R. Forsati and M. Meybodi
in Information and Knowledge Discovery (IKT2007), pp. 235-242, Mashahd, Iran [PDF]
An Algortihm for
Bandwidth Dealy Constrained Multicast Routing
joint work with A. Movaghar, and R. Forsati
in 13th Int'l CSI Computer Conference (CSICC'2008), March 9-11, 2008, Kish Island, Persian Gulf, Iran, Proceeding of Springer Lecture Notes on Computer Communication [PDF]
Web page Clustering
Using Harmony Search Optimization
joint work with R. Forsati, M. Kangavari
in IEEE Canadian Conference on Electrical and Computer Engineering(CCECE 2008), Symposium on Computer Systems and Applications, May 2008, pp. 1601-1604[PDF]
An Efficient
Algorithm for Multicast Routing
joint work with R. Forsati, A.T. Haghighat
in IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 2008), Symposium on Communications and Networking, May 2008, pp. 1641-1646[PDF]
Hybridization of
K-means and Harmony Search Methods for Web Page Clustering
joint work with R. Forsati, M. Meybodi
in IEEE/WIC/ACM International Conference on Web Intelligence (WI-08), Accepted [PDF]
Book Chapters
Harmony Search
Algorithms: Theory and Applications
in Springer (the Studies in Computational Intelligence series)
Technical Reports
Bsc Thesis:
Evaluation of scheduling algorithms in Grid Computing environments[PDF]
Under Supervision of Dr. Hosseini Nezhad
Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic)
Msc Thesis:
Parallel coarse-grained ranking of web pages[PDF]
Under Supervision of Prof. Mohammad Ghodsi
Department of Computer Engineering, Sharif University of Technology