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#### Finding the sensors location and the number of sensors in sensor networks with a genetic algorithm

, Article Proceedings of the 2008 16th International Conference on Networks, ICON 2008, 12 December 2008 through 14 December 2008, New Delhi ; 2008 ; 9781424438051 (ISBN) ; Mohammadi, M ; Movaghar, A ; Dehghan, M ; Sharif University of Technology
2008

Abstract

Sensor networks have recently emerged as a premier research topic. Sensor networks pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. While designing the sensor networks according to performed computation, the limited number of sensors to cover an area will be considered, so the proper placing of this limited number of sensors will cause costs to reduce regarding to coverage and development of the network in the next stage. In this paper we will present a genetic algorithm to solve the designing issue of the sensor network. The most important...

#### Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data

, Article Pattern Recognition ; Volume 43, Issue 8 , August , 2010 , Pages 2982-2992 ; 00313203 (ISSN) ; Bagheri Shouraki, S ; Sharif University of Technology
2010

Abstract

The problem of clustering with side information has received much recent attention and metric learning has been considered as a powerful approach to this problem. Until now, various metric learning methods have been proposed for semi-supervised clustering. Although some of the existing methods can use both positive (must-link) and negative (cannot-link) constraints, they are usually limited to learning a linear transformation (i.e., finding a global Mahalanobis metric). In this paper, we propose a framework for learning linear and non-linear transformations efficiently. We use both positive and negative constraints and also the intrinsic topological structure of data. We formulate our metric...

#### Optimal production and maintenance control under a time variant demand

, Article Scientia Iranica ; Volume 12, Issue 1 , 2005 , Pages 26-33 ; 10263098 (ISSN) ; Sharif University of Technology
Sharif University of Technology
2005

Abstract

In this paper, optimal production and maintenance planning of a flexible manufacturing system under a time variant demand is considered. There is a preventive maintenance plan to reduce the failure rate of the machine. It is assumed that the failure rate of the machine is a function of its age and its maintenance rate. It is, also, assumed that the demand of the manufacturing product is time dependent and its rate depends on the level of the advertisement on that product, The objective is to maximize the expected discounted total profit of the firm over an infinite time horizon. To solve this optimization problem, first, an optimal control is characterized by a set of Hamilton-Jacobi-Bellman...

#### Euclidean movement minimization

, Article Proceedings of the 23rd Annual Canadian Conference on Computational Geometry, CCCG 2011, 10 August 2011 through 12 August 2011 ; February , 2011 ; Fazli, M ; Ghodsi, M ; Khalilabadi, P. J ; Safari, M ; Sharif University of Technology
2011

Abstract

We consider a class of optimization problems called movement minimization on euclidean plane. Given a set of nodes on the plane, the aim is to achieve some spe- cific property by minimum movement of the nodes. We consider two specific properties, namely the connectiv- ity (Con) and realization of a given topology (Topol). By minimum movement, we mean either the sum of all movements (Sum) or the maximum movement (Max). We obtain several approximation algorithms and some hardness results for these four problems. We obtain an O(m)-factor approximation for ConMax and ConSum and an O( p m=OPT)-factor approximation for Con- Max. We also extend some known result on graphical grounds in [1, 2] and...

#### A competitive optimization approach for data clustering and orthogonal non-negative matrix factorization

, Article 4OR ; 2020 ; Mahdavi Amiri, N ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2020

Abstract

Partitioning a given data-set into subsets based on similarity among the data is called clustering. Clustering is a major task in data mining and machine learning having many applications such as text retrieval, pattern recognition, and web mining. Here, we briefly review some clustering related problems (k-means, normalized k-cut, orthogonal non-negative matrix factorization, ONMF, and isoperimetry) and describe their connections. We formulate the relaxed mean version of the isoperimetry problem as an optimization problem with non-negative orthogonal constraints. We first make use of a gradient-based optimization algorithm to solve this kind of a problem, and then apply a post-processing...

#### A general purpose optimization approach

, Article 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 25 September 2007 through 28 September 2007 ; 2007 , Pages 4538-4545 ; 1424413400 (ISBN); 9781424413409 (ISBN) ; Showaki, S. B ; Heravi, M. J ; Jashmi, B. J ; Sharif University of Technology
2007

Abstract

Recombination in the Genetic Algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations hopefully producing an offspring that has the good characteristics of both parents and this requires explicit chromosome and recombination operator design. This paper presents a novel evolutionary approach based on symbiogenesis which uses symbiotic combination instead of sexual recombination and using this operator, it requires no domain knowledge for chromosome or combination operator design. The algorithm is benchmarked on three problem sets, combinatorial optimization, deceptive, and fully deceptive, and is compared with standard...

#### A metaheuristic approach to the graceful labeling problem of graphs

, Article 2007 IEEE Swarm Intelligence Symposium, SIS 2007, Honolulu, HI, 1 April 2007 through 5 April 2007 ; 2007 , Pages 84-91 ; 1424407087 (ISBN); 9781424407088 (ISBN) ; Eshghi, K ; Sharif University of Technology
2007

Abstract

In this paper, an algorithm based on Ant Colony Optimization metaheuristic is proposed for finding solutions to the well-known graceful labeling problem of graphs. Despite the large number of papers published on the theory of this problem, there are few particular techniques introduced by researchers for gracefully labeling graphs. The proposed algorithm is applied to many classes of graphs, and the results obtained have proven satisfactory when compared to those of the existing methods in the literature. © 2007 IEEE

#### Stochastic control of two-level nonlinear large-scale systems; Part II-Interaction balance principle

, Article 2006 IEEE International Conference on Systems, Man and Cybernetics, Taipei, 8 October 2006 through 11 October 2006 ; Volume 5 , 2006 , Pages 3856-3861 ; 1062922X (ISSN); 1424401003 (ISBN); 9781424401000 (ISBN) ; Dehghan Marvast, E ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2006

Abstract

In this paper a new two-level method for stochastic control of large-scale systems is presented. This two-level approach is based on Interaction Balance Principle (Goal coordination) for optimization problem and a two-level computational algorithm using extended Kalman filter for the estimation one. The two-level optimizer uses a new coordinator which works by the gradient of interaction errors that improves the convergence rate of the solution. The significance and the applicability of the theoretical developments of this paper are also shown by a numerical example. © 2006 IEEE

#### A genetic algorithm for resource investment problem with discounted cash flows

, Article Applied Mathematics and Computation ; Volume 183, Issue 2 , 2006 , Pages 1057-1070 ; 00963003 (ISSN) ; Akhavan Niaki, S. T ; Sharif University of Technology
2006

Abstract

A resource investment problem with discounted cash flows is a project scheduling problem in which the availability levels of the resources are considered decision variables and the goal is to find a schedule and resource requirement levels such that the net present value of the project cash flows optimizes. In this paper, we present a genetic algorithm to solve this problem. We explain the elements of the algorithm such as chromosome structure, fitness function, crossover, mutation, and local improvement operations and solve more than 220 problems with known optimal solutions to evaluate the performance of the proposed algorithm. The results of the experimentation are quite satisfactory. ©...

#### A competitive optimization approach for data clustering and orthogonal non-negative matrix factorization

, Article 4OR ; Volume 19, Issue 4 , 2021 , Pages 473-499 ; 16194500 (ISSN) ; Mahdavi Amiri, N ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2021

Abstract

Partitioning a given data-set into subsets based on similarity among the data is called clustering. Clustering is a major task in data mining and machine learning having many applications such as text retrieval, pattern recognition, and web mining. Here, we briefly review some clustering related problems (k-means, normalized k-cut, orthogonal non-negative matrix factorization, ONMF, and isoperimetry) and describe their connections. We formulate the relaxed mean version of the isoperimetry problem as an optimization problem with non-negative orthogonal constraints. We first make use of a gradient-based optimization algorithm to solve this kind of a problem, and then apply a post-processing...

#### A new stabilizing GPC for nonminimum phase LTI systems using time varying weighting

, Article Wec 05: Fourth World Enformatika Conference, Istanbul, 24 June 2005 through 26 June 2005 ; Volume 6 , 2005 , Pages 156-158 ; 9759845857 (ISBN); 9789759845858 (ISBN) ; Haeri, M ; Sharif University of Technology
2005

Abstract

In this paper, we show that the stability can not be achieved with current stabilizing MPC methods for some unstable processes. Hence we present a new method for stabilizing these processes. The main idea is to use a new time varying weighted cost function for traditional GPC. This stabilizes the closed loop system without adding soft or hard constraint in optimization problem. By studying different examples it is shown that using the proposed method, the closed-loop stability of unstable nonminimum phase process is achieved. COPYRIGHT © ENFORMATIKA

#### An extension of ant colony system to continuous optimization problems

, Article 4th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2004, Brussels, 5 September 2004 through 8 September 2004 ; Volume 3172 LNCS , 2004 , Pages 294-301 ; 03029743 (ISSN); 3540226729 (ISBN); 9783540226727 (ISBN) ; Nobahari, H ; Sharif University of Technology
Springer Verlag
2004

Abstract

A new method for global minimization of continuous functions has been proposed based on Ant Colony Optimization. In contrast with the previous researches on continuous ant-based methods, the proposed scheme is purely pheromone-based. The algorithm has been applied to several standard test functions and the results are compared with those of two other meta-heuristics. The overall results are compatible, in good agreement and in some cases even better than the two other methods. In addition the proposed algorithm is much simpler, which is mainly due to its simpler structure. Also it has fewer control parameters, which makes the parameter settings process easier than many other methods. © 2004...

#### Orthogonal nonnegative matrix factorization problems for clustering: A new formulation and a competitive algorithm

, Article Annals of Operations Research ; 2022 ; 02545330 (ISSN) ; Mahdavi Amiri, N ; Sharif University of Technology
Springer
2022

Abstract

Orthogonal Nonnegative Matrix Factorization (ONMF) with orthogonality constraints on a matrix has been found to provide better clustering results over existing clustering problems. Because of the orthogonality constraint, this optimization problem is difficult to solve. Many of the existing constraint-preserving methods deal directly with the constraints using different techniques such as matrix decomposition or computing exponential matrices. Here, we propose an alternative formulation of the ONMF problem which converts the orthogonality constraints into non-convex constraints. To handle the non-convex constraints, a penalty function is applied. The penalized problem is a smooth nonlinear...

#### A new real-coded Bayesian optimization algorithm based on a team of learning automata for continuous optimization

, Article Genetic Programming and Evolvable Machines ; Vol. 15, Issue. 2 , 2014 , pp. 169-193 ; ISSN: 13892576 ; Beigy, H ; Sharif University of Technology
Abstract

Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. Bayesian optimization algorithm is an estimation of distribution algorithm, which uses a Bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the Bayesian optimization algorithms are capable of identifying correct linkage between the variables of optimization problems. Since the problem of finding the...

#### Multi-label classification with feature-aware implicit encoding and generalized cross-entropy loss

, Article 24th Iranian Conference on Electrical Engineering, 10 May 2016 through 12 May 2016 ; 2016 , Pages 1574-1579 ; 9781467387897 (ISBN) ; Soleymani Baghshah, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc

Abstract

In multi-label classification problems, each instance can simultaneously have multiple labels. Since the whole number of available labels in real-world applications tends to be (very) large, multi-label classification becomes an important challenge and recently label space dimension reduction (LSDR) methods have received attention. These methods first encode the output space to a low-dimensional latent space. Afterwards, they predict the latent space from the feature space and reconstruct the original output space using a suitable decoding method. The encoding method can be implicit which learns a code matrix in the latent space by solving an optimization problem or explicit which learns a...

#### Recovery of missing samples using sparse approximation via a convex similarity measure

, Article 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017, 3 July 2017 through 7 July 2017 ; 2017 , Pages 543-547 ; 9781538615652 (ISBN) ; Zayyani, H ; Marvasti, F ; Anbarjafari, G ; Kivinukk, A ; Tamberg, G ; Sharif University of Technology
Abstract

In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image processing and for this purpose, we suggest an iterative sparse recovery algorithm based on constrained l1-norm minimization with a new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index, is a simplified version of the Structural SIMilarity (SSIM) index, which is convex and error-sensitive. The optimization problem incorporating this criterion, is then...

#### Interference efficiency: A new concept to analyze the performance of cognitive radio networks

, Article 2017 IEEE International Conference on Communications Workshops, ICC Workshops 2017, 21 May 2017 through 25 May 2017 ; 2017 , Pages 1105-1110 ; 9781509015252 (ISBN) ; Musavian, L ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2017

Abstract

In this paper, we develop and analyze a novel performance metric, called interference efficiency (IE), that shows the number of transmitted bits per unit of interference energy imposed on the primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we develop a framework to maximize the IE of a CRN with multiple secondary users (SUs) while satisfying target constraints on the average interference power on PU receiver, total SUs transmit power and minimum ergodic rate for the SUs. In doing so, we formulate a multiobjective optimization problem (MOP), that aims to achieve the maximum ergodic sum rate of multiple SUs and the minimum average interference power on the...

#### Optimization of the electric arc furnace process

, Article IEEE Transactions on Industrial Electronics ; Volume 66, Issue 10 , 2019 , Pages 8030-8039 ; 02780046 (ISSN) ; Fathi, A ; Skrjanc, I ; Logar, V ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019

Abstract

This paper presents an electric arc furnace (EAF) optimization framework intended to define optimal control profiles for the EAF, in order to increase its efficiency and thus reduce the energy consumption. The framework aims to minimize controllable losses and to maximize energy transfer to the bath and, consequently, minimize the operational costs. This is achieved through improved actuation of the EAF inputs, i.e., transformer power, oxygen lancing, and carbon addition. To achieve maximal energy transfer to the bath and to reduce the heat losses from the arcs, proper properties of the slag, such as foaminess and basicity, are a subject of considerable attention. The framework is designed...

#### Stochastic successive convex approximation for non-convex constrained stochastic optimization

, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 16 , 2019 , Pages 4189-4203 ; 1053587X (ISSN) ; Lau, V. K. N ; Kananian, B ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019

Abstract

This paper proposes a constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point for a general non-convex stochastic optimization problem, whose objective and constraint functions are non-convex and involve expectations over random states. Most existing methods for non-convex stochastic optimization, such as the stochastic (average) gradient and stochastic majorization-minimization, only consider minimizing a stochastic non-convex objective over a deterministic convex set. The proposed CSSCA algorithm can also handle stochastic non-convex constraints in optimization problems, and it opens the way to solving more challenging optimization problems that...

#### Secure transmission with covert requirement in untrusted relaying networks

, Article 9th International Symposium on Telecommunication, IST 2018, 17 December 2018 through 19 December 2018 ; 2019 , Pages 670-675 ; 9781538682746 (ISBN) ; Azmi, P ; Kuhestani, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019

Abstract

In this paper, we investigate the problem of secure communication with covert requirement in untrusted relaying networks. Our considered system model contains one source, one destination, one untrusted relay, and one Willie. The untrusted relay tries to extract the information signal, while the goal of Willie is to detect the presence of the information signal transmitted by the source, in the current time slot. To overcome these two attacks, it is assumed that the destination and the source inject jamming signal to the network in phase I and phase II, respectively. Accordingly, the communication in our proposed system model is accomplished in two phases. In the first phase, when the source...