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2014
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Portfolio Optimization Using Particle Swarm Optimization and Genetic Algorithm
Portfolio Optimization Using Particle Swarm Optimization and Genetic Algorithm
en
en
This study basically employs the Markowitz mean–variance model for portfolio selection problem. Since this model is classified as a quadratic programming model there is not any efficient algorithm to solve it. The goal of this study is to find a feasible portfolio with a minimum risk through the application of heuristic algorithm. The two PSO and GA algorithm has been used. The results show that PSO approach is suitable in portfolio optimization.
85
90
Samira
Kamali
Portfolio optimization
Particle Swarm Optimization
Generic Algorithm.
Article.1.pdf
[
[1]
T. J. Chang, S. Yang, K. Chang, Portfolio optimization problems in different risk measures using genetic algorithm, Expert Systems with Applications, 36 (2009), 10529-10537
##[2]
T. Cura, Particle swarm optimization approach to portfolio optimization, Nonlinear Analysis Real World Applications, 10 (2009), 2396-2406
##[3]
D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA (1989)
##[4]
H. R. Golmakani, M. Fazel , Constrained Portfolio Selection using Particle Swarm Optimization, Expert Systems with Applications, 38 (2011), 8327-8335
##[5]
R. Khalesi, H. Maleki, A new method for solving fuzzy MCDM problems, Journal of mathematics and computer Science, 1 (2010), 238-438
##[6]
A. Loraschi, A. Tettamanzi, M. Tomassini, C. Svizzero, C. Scientifico, P. Verda, Distributed genetic algorithms with an application to portfolio selection, Proceedings of the international conference on artificial neural networks and genetic algorithms, ICANNGA95, (1995), 384-387
##[7]
H. Markowitz , Portfolio selection, efficient diversification of investments, New York , Wiley (1959)
##[8]
H. Markowitz, The optimization of a quadratic function subject to linear constraints, Naval Research Logistics Quarterly, 3 (1956), 111-133
##[9]
H. Markowitz, Portfolio selection, Journal of Finance, 7 (1952), 77-91
##[10]
F. Matroud, H. Sadeghi, Solving bi-level programming with multiple linear objectives at lower level using particle swarm optimization, Journal of mathematics and computer science, 7 (2013), 221-229
##[11]
M. Rostami, M. Kianpour, E. bashardoust, A numerical algorithm for solving nonlinear fuzzy differential equations, Journal of mathematics and computer Science, 177 (2007), 3397-3410
##[12]
H. Soleimani, Portfolio selection using genetic algorithm, MS Degree Thesis, Amirkabir University of Technology, Industrial Engineering Department, Tehran (2007)
##[13]
H. Soleimani, H. R. Golmakani, M. H. Salimi , Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm, Expert Systems with Applications, 36 (2009), 5058-5063
##[14]
P. Wolfe, The simplex method for quadratic programming, Econometrica, 27 (1959), 382-398
##[15]
F. Xu, W. Chen, L. Yang, Improved Particle Swarm Optimization for realistic portfolio selection , In Eighth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, IEEE Computer Society, (2007), 185-190
]
Presentation the Decision Making Model, in Order to the Optimality of Using Solar Panels and Wind Turbines Instead of Usual Power in Places with Limited Use
Presentation the Decision Making Model, in Order to the Optimality of Using Solar Panels and Wind Turbines Instead of Usual Power in Places with Limited Use
en
en
In general, as a source of lightening, heat and cold and in particular, in business and industry, electricity can be considered as the most commonly applied energy in the communities. The researcher, scholars and policymakers are required to see into the future of electricity because it has been significantly demanded as an efficient fuel for economic growth and development with no pollution. Having reviewed various models of electric energy, the current research is intended to present a mathematical model in order to evaluate the utilization of wind, solar and power plants for the places with limited use, the results showed that using such plants from the national network of electricity.
91
99
Hesamoddin
Salaryan
Hadi
Kashefi
Solar energy
wind energy
cost model
Article.2.pdf
[
[1]
A. L. B. Heagle, G. F. Naterer, K. Pope, Small wind turbine energy policies for residential and small business usage in Ontario, Canada, Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, 2000 Simcoe Street North, Oshawa, Ontario, Canada L1H 7K4, Energy Policy 39 (2011)
##[2]
Martin J. Pasqualetti, Susan Haag, A solar economy in the American Southwest, School of Geographical Sciences and Urban Planning, Coor Hall, 5th Floor, 975 South Myrtle Avenue, Arizona State University, Tempe, AZ 85287-5301, United States, Energy Policy, 39 (2011), 887-893
##[3]
Yue ming Qiu, D. Laura , The price of wind power in China during its expansion, Energy Economics xxx , Anadon Stanford University, AThe Jerry Yang and Akiko Yamazaki, Environment & Energy Building, 473 Via Ortega, Stanford, CA 94305, USA, tmosphere/Energy Program (2011)
##[4]
Raul Moraisa, Samuel G. Matosb, Miguel A. Fernandesb, Antonio L. G. Valentea, Salviano F. S. P. Soaresa, P. J. S. G. Ferreirac, M. J. C. S. Reisa, Sun, wind and water flow as energy supply for small stationary data acquisition platforms, computers and electronics in agriculture, 64 (2008), 120-132
##[5]
A. L. B. Heagle, G. F. Naterer, K. Pope, Small wind turbine energy policies for residential and small business usage in Ontario, Canada, Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, 2000 Simcoe Street North, Oshawa, Ontario, Canada L1H 7K4, Energy Policy 39 (2011)
##[6]
F. Moini, an estimation of solar radiation for using solar plants in Iran, Iranian Journal of Energy, Volume 13,No.2 (2011)
##[7]
Samira Monshipour, an economic review of the photovoltaic systems to supply the electricity of the villages without power, Sixth National Conference on Energy, Iran (2008)
##[8]
, , http://www.solarbuzz.com, ()
]
On Bivariate Haar Functions and Interpolation Polynomial
On Bivariate Haar Functions and Interpolation Polynomial
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en
In this paper we consider bivariate Haar series in general case, where bivariate Haar functions are defined on the plane. Here we define a new bivariate Haar function that is included two independent variables. Indeed we presented the new function that is not in previous researches. Mathematicians have applied bivariate Haar function based on tensor product that is a special case of bivariate case. In this research we define the Haar functions by applying another way. Therefore, we define the Haar function differently. And also, the interpolation polynomial with two variables is explained. Then we compare two methods for calculating the approximating function. Namely, we consider a numerical example for comparing the new approximation to bivariate interpolation polynomial. In this example we compute interpolation polynomial by points with Newton lattice form. The calculations indicate that the accuracy of the obtained solutions is acceptable when the number of calculation points is small.
100
112
R.
Dehghan
K. Rahsepar
Fard
Bivariate Haar function
Bivariate interpolation polynomial
Haar Fourier coefficient
Haar series
Article.3.pdf
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]
An Application of Co-medial Algebras with Quasigroup Operations on Cryptology
An Application of Co-medial Algebras with Quasigroup Operations on Cryptology
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en
A modification of Markovski quasigroup based crypto-algorytm has been presented. This modification is based on the pair of co-medial quasigroup operations, which we show that they are orthogonal quasigroup operations.
113
118
Amir
Ehsani
co-medial pair of operations
quasigroup operation
orthogonal operations
cryptology
cipher-text
enciphering.
Article.4.pdf
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[1]
V. D. Belousov, The group associated with a quasigroup, Mat. Issled., 4(3) (1969), 21-39
##[2]
A. Ehsani, The Generalized entropic property for the pair of operations, Journal of ContemporaryMathematical Analysis , 46(1) (2011), 29-34
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A. Ehsani , On Regular Co-medial Algebras, J. Mathematics Research , 4(2) (2012), 101-109
##[4]
A. Ehsani, On Medial-like Functional Equations, Mathematical Problems of ComputerScience , 38 (2012), 53-55
##[5]
A. Ehsani, Yu. M. Movsisyan, Linear Representation of Medial-like Algebras, Communications in Algebra , 41(9) (2013), 3429-3444
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S. H. Kamali, M. Hedayati, R. Shakerian, S. Ghasempour, Using Identity-Based Secret Public Keys Cryptography for Heuristic Security Analyses in Grid Computing, The Journal of Mathematics and Computer Science , 3(4) (2011), 357-375
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]
Designing a New Version of Ant-miner Using Genetic Algorithm
Designing a New Version of Ant-miner Using Genetic Algorithm
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en
The current article seeks to design and implement a new algorithm for data mining based on ant colony optimization algorithm, which is called Ant-Miner. Ant-Miner extracts classification rules from databases. In our article, we have presented a new version of Ant-Miner which is more efficient than its previous versions. The new version has been dubbed "Ant-Miner 4". We have modified the structure of the heuristic function used in Ant-Miner, implemented it based on the correction function of Laplace, and changed pheromone trail synchronization process in order to enable the redesigned system to produce rules with higher prediction power. In the proposed algorithm, we have tried to employ genetic algorithm to avoid local minimum points, produce a general optimized response, and determine the best values for the parameters. We tested Ant-Miner 4 and Ant-Miner 3 on four data sets, finding out that the new Ant-Miner has a better performance than the older version in terms of the accuracy of the extracted rules.
119
130
Kayvan
Azaryuon
ant colony optimization algorithm
classification rules
data mining
databases.
Article.5.pdf
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S. M. Weiss, C. A. Kulikowski, Computer Systems that Learn, San Francisco, CA: Morgan Kaufmann (1991)
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A. A. Freitas, S. H. Lavington, Mining Very Large Databases with Parallel Processing, London, UK: Kluwer (1998)
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M. Dorigo, G. Di Caro, L. M. Gambardella, Antalgorithms for discrete optimization, Artificial Life, 5 (2000), 137-172
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M. Dorigo, V. Maniezzo, The ant system: optimizationby a colony of cooperating agents, IEEE Transactions on Systems,Man, and Cybernetics, 26(1) (1996), 1-13
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Ziqiang Wang, Boqin Feng, Classification Rule Mining with an Improved Ant Colony Algorithm, Lecture Notes in Computer Science, Volume 3339 (2004)
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M. Dorigo, A. Colorni, V. Maniezzo, The Ant System: optimization by a colony of cooperating gents, IEEE Transactions onSystems, Man, andCybernetics-Part B, 26 (1996), 29-41
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M. P. Oakes, Ant Colony Optimization for Stylometry: The Fedaralist Papers, International Conference on Recent Advances in Soft Computing, (2004)
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Bo Liu, Hussein A. Abbass, Bob McKay, Classification Rule Discovery with Ant Colony Optimization , IEEE Computational Intelligence Bulletin February , (2004)
##[10]
Lotti Admane, Karima Benatchba, Mouloud KOUDIL, Habiba, Using ant colonies to solve data-mining problems, DFUAS IEEEInternational Conhence on SystemsBPM, 16270, Oued Smar, Algrie (2003)
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R. S. Parepinelli, Lopes, An Ant Colony Algorithm for Classification Rule Discovery, In H.A. a. R. S. a. C. Newton (Ed.), Data Mining: Heuristic Approach: Idea Group Publishing (2002)
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M. P. Oakes, Ant Colony Optimisation for Stylometry: The Fedaralist Papers, International Conference on Recent Advances in Soft Computing, (2004)
##[14]
R. Shakerian, S. H. Kamali, M. Hedayati, M. Alipour, Comparative Study of Ant Colony Optimization and Particle Swarm optimization for Gris Scheduling, TJMCS , 3 (2011), 469-474
##[15]
Rouhollah Maghsoudi, Arash Ghorbannia Delavar, Somayye Hoseyny, Rahmatollah Asgari, Yaghub Heidari , Representing the New Model for Improving K-Means Clustering Algorithm based on Genetic Algorithm, Journal of mathematics and computer Science(JMCS) , 2 (2011), 329-336
]
Cyclic Edge Extensions Self-centered Graphs
Cyclic Edge Extensions Self-centered Graphs
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en
The eccentricity e(u) of a vertex u is the maximum distance of u to any other vertex of G. The maximum and the minimum eccentricity among the vertices of a graph G are known as the diameter and the radius of G respectively. If they are equal then the graph is said to be a self - centered graph. Edge addition /extension to a graph either retains or changes the parameter of a graph, under consideration. In this paper mainly, we consider edge extension for cycles, with respect to the self-centeredness(of cycles),that is, after an edge set is added to a self centered graph the resultant graph is also a self-centered graph. Also, we have other structural results for graphs with edge -extensions.
131
137
Medha Itagi
Huilgol
Chitra
Ramprakash
Self centered graphs
Edge extension graphs
reduced radius
reduced diameter of cycles
Iterations of cycles and paths.
Article.6.pdf
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Akira Saito, Cycles of length 2 modulo 3 in graphs, Discrete Mathematics, 101 (1992), 285-289
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Akira Saito, Gantao Chen, Graphs with a cycle of length divisible by three, Journal of Combinatorial theory, 60 (1994), 277-292
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Akira Saito, Linda Lesniak, Nathaniel Dean, Cycles of length 0 modulo 4 in graphs, Discrete Mathematics, 121 (1993), 37-49
##[4]
Akram B. Attar, Edge Extension of Graphs and Digraphs, The Journal of Mathematics and Computer Science, 3 (2011), 1-10
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Akram B. Attar, Extensibility of Graphs, Journal of Applied Mathematics, Islamic Azad University of Lahijan, 6 (2009), 1-9
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F. Buckley, F. Harary, Distance in Graphs, Addison Wesley, (1990)
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T. N. Janakiraman, M. Bhanumathi, S. Muthammai, Self - centered super graph of a graph and center number of a graph, Ars Combinatorica, 87 (2008), 271-290
##[10]
H. B. Walikar, F. Buckley, M. K. Itagi, Radius - edge - invariant and diameter - edge- invariant graphs, Discrete Mathematics, 272(1) (2001), 119-126
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H. B. Walikar, F. Buckley, M. K. Itagi, Diameter essential edges in a graph, Discrete Mathematics, 259 (2002), 211-225
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H. B. Walikar, F. Buckley, M. K. Itagi, Radius essential edges in a graph, Journal of Combinatorial Mathematics and Combinatorial Computing, 53 (2005), 209-220
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H. B. Walikar, F. Buckley, Medha Itagi Huilgol , Diameter vital edges in a graph, Aeqationes Mathematicae, 82 (2011), 201-211
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]
Note on T-derivations of B-algebras
Note on T-derivations of B-algebras
en
en
In this paper, we introduce the notion of \(t\)-derivation on \(B\)-algebras and obtain some of its related properties.
138
143
Rasoul
Soleimani
Somayeh
Jahangiri
\(B\)-algebra
\(O\)-commutative
\(t\)-derivation.
Article.7.pdf
[
[1]
H. A. S. Abujabal, N. O. Al-Shehrie, On left Derivations of \(BCI\)-algebras, Soochow J. Math., 33(3) (2007), 435-444
##[2]
N. O. Al-Shehrie, Derivations of \(B\)-algebras, JKAU: Sci., 22(1) (2010), 71-83
##[3]
J. R. Cho, H. S. Kim, On \(B\)-algebras and quasigroups, Quasigroups and Related Systems, 8 (2001), 1-6
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Y. Imai, K. Iseki, On axiom systems of propositional calculi. XIV, Proc. Japan Acad., 42 (1966), 19-22
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K. Iseki, An algebra related with a propositional calculus, Proc. Japan Acad., 42 (1966), 26-29
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H. S. Kim, H. G. Park, On \(0\)-commutative \(B\)-algebras, Sci. Math. Japonicae Online, (2005), 31-36
##[7]
J. Neggers, H. S. Kim, On \(B\)-algebras, Mate. Vesnik, 54 (2002), 21-29
##[8]
S. A. Nematoalah Zadeh, A. Radfar, A. Borumand Saied, On \(BP\)-algebras and \(QS\)-algebras, TJMCS, 5(1) (2012), 17-21
##[9]
A. Walendziak, Some axiomatizations of \(B\)-algebras, Math. Slovaca, 56(3) (2006), 301-306
]
Introduce a New Algorithm for Data Clustering by Genetic Algorithm
Introduce a New Algorithm for Data Clustering by Genetic Algorithm
en
en
Clustering of data into adequate categories is one of the most important issues in pattern recognition. What is important in clustering, doing so is no predetermined pattern, provided that the same data should be in a category. In this paper, first, a clustering method using a grouping genetic algorithm (GGA) to describe, then the proposed model we introduce and the proposed method are tested on several sets of data and finally we compare the proposed method with the GGA algorithm.
The results show that the proposed algorithm is well-GGA gives us the answer and in terms of time and space complexity are much better than GGA.
144
156
J.
Vahidi
S.
Mirpour
grouping genetic algorithm
clustering
pattern recognition
Article.8.pdf
[
[1]
C. C. Aggarwal, J. Han, J. Wang, P. S. Yu, A framework for clustering evolving data streams, Proceedings of the 29th VLDB Conference, (2003)
##[2]
L. E. Agustin-Blas, S. Salcedo-Sanz, S. Jimenez-Fernandez, L. Carro-Calvo, J. Del Ser , A new grouping genetic algorithm for clustering problems, Expert Systems with Applications , 39 (2012), 9695-9703
##[3]
Daniel Barbard, Requirements for Clustering Data Streams, ACM SIGKDD Explorations Newsletter, 3 (2002), 23-27
##[4]
Jurgen Beringer, Eyke Hullermerier, Fuzzy Clustering of Parallel Data streams, Data & Knowledge Engineering , (2006), 180-204
##[5]
Albert Bifet, Geo Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl , MOA: Massive Online Analysis, for Stream Classification and Clustering, , (2010)
##[6]
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kandel, Info-fuzzy algorithms for mining dynamic data streams, Applied Soft Computing, (2008), 1283-1294
##[7]
M. M. Gaber, Arkady Zaslavsky, Shonali Krishnaswamy, Mining Data Streams: A Review, SIGMOD Record, vol. 34, no. 2 (2005)
##[8]
Mohammad GhasemiGol, Hadi Sadoghi Yazdi, Reza Monsefi, A New Hierarchical Clustering Algorithm on Fuzzy Data (FHCA), International Journal of Computer and Electrical Engineering, vol. 2, no. 1 (2010)
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Richard J. Hathaway, James C. Bezdek, Extending Fuzzy and Probabilistic Clustering to Very Large Data Sets, Journal of Computational Statistics and Data Analysis, 51 (2006), 215-234
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Madjid Khalilian, Norwati Mustapha, Data Stream Clustering, Challenges and Issues, (2010)
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Alireza Rezaei Mahdiraji, Clustering data streams: A survey of algorithms, International Journal of Knowledge-based and Intelligent Engineering Systems, (2009), 39-44
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Mohamed Medhat Gaber, Arkady Zaslavsky, Shonali Krishnaswamy, Mining Data Streams: A Review, SIGMOD Record, vol. 34, no. 2 (2005)
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D. P. Mercer, Linacre College, Clustering large datasets, , (2003)
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Liadan O’Chalaghan, Nina Mishra, Adam Meyerson,Sudipto Guha, Rajeev Motwani, Streaming data algorithms for high quality clustering, Proc. of IEEE International Conference on Data Engineering, (2002), 685-694
##[18]
Nikhil R. Pal, Kuhu Pal, James M. Keller, James C. Bezdek, A Possibilistic Fuzzy c-Means Clustering Algorithm, IEEE Transactions on Fuzzy Systems, 517 - 530 (2005)
##[19]
Renxia Wan, Xiaoya Yan, Xiaoke Su, A Weighted Fuzzy Clustering Algorithm for Data Stream, presented at ISECS International Colloquium on Computing, Communication, Control, and Management.CCCM ()
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Xuanli Lisa Xie, Gerardo Beni, A Validity Measure for Fuzzy Clustering, , (1990)
##[21]
T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: An efficient data clustering method for very large databases, , ()
]