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mahdavi-amiri--ali
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Heuristic Hybrid Genetic and Simulated Annealing Algorithms with Neural Networks for Task Assignment in Heterogeneous Computing Systems
, M.Sc. Thesis Sharif University of Technology ; Mahdavi Amiri, Nezamoddin (Supervisor)
Abstract
In this thesis, we want to present methods that are able to solve the assignment tasks problem in a heterogeneous computing system. These methods are two hybrid methods that are constructed by composing Hopefield Neural Networks with Genetic Algorithms and the Simulated Annealing. First, we solve the relaxed problem by applying Genetic Algorithms and the Simulated Annealing and we compare the results of these ways with other traditional methods. Then, we solve the constrained problem with mentioned hybrid methods. The definition of the problem is as following: Consider a distributed computing system which is comprised of set of processors with different speeds but the same structure. We want...
A modified ant colony system for finding the expected shortest path in networks with variable arc lengths and probabilistic nodes
, Article Applied Soft Computing Journal ; Vol. 21, issue , August , 2014 , p. 491-500 ; Hassanzadeh, R ; Mahdavi, I ; Mahdavi-Amiri, N ; Sharif University of Technology
2014
Abstract
The problem of finding the expected shortest path in stochastic networks, where the presence of each node is probabilistic and the arc lengths are random variables, have numerous applications, especially in communication networks. The problem being NP-hard we use an ant colony system (ACS) to propose a metaheuristic algorithm for finding the expected shortest path. A new local heuristic is formulated for the proposed algorithm to consider the probabilistic nodes. The arc lengths are randomly generated based on the arc length distribution functions. Examples are worked out to illustrate the applicability of the proposed approach
A genetic algorithm for optimization problems with fuzzy relation constraints using max-product composition
, Article Applied Soft Computing Journal ; Volume 11, Issue 1 , 2011 , Pages 551-560 ; 15684946 (ISSN) ; Khorram, E ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
2011
Abstract
We consider nonlinear optimization problems constrained by a system of fuzzy relation equations. The solution set of the fuzzy relation equations being nonconvex, in general, conventional nonlinear programming methods are not practical. Here, we propose a genetic algorithm with max-product composition to obtain a near optimal solution for convex or nonconvex solution set. Test problems are constructed to evaluate the performance of the proposed algorithm showing alternative solutions obtained by our proposed model
A genetic optimization algorithm and perceptron learning rules for a bi-criteria parallel machine scheduling
, Article Journal of the Chinese Institute of Industrial Engineers ; Volume 29, Issue 3 , 2012 , Pages 206-218 ; 10170669 (ISSN) ; Hassanzadeh, R ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
2012
Abstract
This work considers scheduling problems minding the setup and removal times of jobs rather than processing times. For some production systems, setup times and removal times are so important to be considered independent of processing times. In general, jobs are performed according to the automatic machine processing in production systems, and the processing times are considered to be constant regardless of the process sequence. As the human factor can influence the setup and removal times, when the setup process is repetitive the setup times decreases. This fact is considered as learning effect in scheduling literature. In this study, a bi-criteria m-identical parallel machines scheduling...
A DEA approach for comparing solution efficiency in U-line balancing problem using goal programming
, Article International Journal of Advanced Manufacturing Technology ; Volume 61, Issue 9-12 , August , 2012 , Pages 1161-1172 ; 02683768 (ISSN) ; Hassanzadeh, R ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
Springer
2012
Abstract
Line balancing problem plays an important role in the decision making process to increase efficiency and productivity. Recently, U-shaped layout in many production lines has replaced the traditional straight line layout using just-in-time concept. Here, we propose a model, using multi-objective decision making approach to the U-shaped line balancing problem, to offer enhanced decision maker flexibility, by allowing for conflicting goals. The assembly line operation efficiency is the most significant aim in our study, and this efficiency relates to management of resources and the solution of line balancing problem. First, the U-shaped line balancing problem is solved considering the model's...
A dynamic programming approach for finding shortest chains in a fuzzy network
, Article Applied Soft Computing Journal ; Volume 9, Issue 2 , 2009 , Pages 503-511 ; 15684946 (ISSN) ; Nourifar, R ; Heidarzade, A ; Mahdavi Amiri, N ; Sharif University of Technology
2009
Abstract
Graph theory has numerous applications to problems in systems analysis, operations research, transportation, and economics. In many cases, however, some aspects of a graph-theoretic problem may be uncertain. For example, the vehicle travel time or vehicle capacity on a road network may not be known exactly. In such cases, it is natural to make use of fuzzy set theory to deal with the uncertainty. Here, we are concerned with finding shortest chains in a graph with fuzzy distance for every edge. We propose a dynamic programming approach to solve the fuzzy shortest chain problem using a suitable ranking method. By using MATLAB, two illustrative examples are worked out to demonstrate the...
A two-phase linear programming methodology for fuzzy multi-objective mixed-model assembly line problem
, Article International Journal of Advanced Manufacturing Technology ; Volume 44, Issue 9-10 , 2009 , Pages 1010-1023 ; 02683768 (ISSN) ; Javadi, B ; Sahebjamnia, N ; Mahdavi Amiri, N ; Sharif University of Technology
2009
Abstract
We develop a fuzzy multi-objective linear programming (FMOLP) model for solving multi-objective mixed-model assembly line problem. In practice, vagueness and imprecision of the goals in this problem make the fuzzy decision-making complicated. The proposed model considers minimizing total utility work, total production rate variation, and total setup cost, using a two-phase linear programming approach. In the first phase, the problem is solved using a max-min approach. The max-min solution not being efficient, in general, we propose a new model in the second phase to maximize a composite satisfaction degree at least as good as the degrees obtained by phase one. To show the effectiveness of...
Reserve capacity of mixed urban road networks, network configuration and signal settings
, Article Intelligent Transportation and Planning: Breakthroughs in Research and Practice ; February , 2018 , Pages 883-906 ; 9781522552116 (EISBN) ; Hassanzadeh, R ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
IGI Global
2018
Abstract
The authors formulate the transportation mixed network design problem (MNDP) as a mixed-integer bi-level mathematical problem, based on the concept of reserve capacity. The upper level goal is to maximize the reserve capacity by signal settings at intersections, determine street direction and increase street capacities via addition of lanes. The lower level problem is a deterministic user equilibrium traffic assignment problem to minimize the user travel time. The model being non-convex, meta-heuristic methods are used to solve the problem. A hybridization of genetic algorithm with simulated annealing and a bee algorithm are proposed. Numerical examples are illustrated to verify the...
Toward sustainable optimization with stackelberg game between green product family and downstream supply chain
, Article Sustainable Production and Consumption ; Volume 23 , 2020 , Pages 198-211 ; Shirazi, B ; Mahdavi, I ; Mahdavi Amiri, N ; Sharif University of Technology
Elsevier B.V
2020
Abstract
Green products are increasingly considered by companies, owing to the significant attention of government regulations, customers’ requests and competitors. Here, we deal with Green Product Families (GPFs) by selecting green components, modules and products which are produced based on the assembling to order (ATO) approach to cover diverse customer needs. Designing a GPFs is important but not sufficient for sustainable optimization. In fact, we need to simultaneously consider the supply chain of a GPF to control the pollution generated in the downstream supply chain. For a sustainable optimization approach, this joint configuration is usually structured based on economic, environmental and...
Application Microarray Technology in Infectious Diseases
, M.Sc. Thesis Sharif University of Technology ; Mahdavi-Amiri, Nezameddin (Supervisor) ; Karami, Ali (Co-Advisor)
Abstract
DNA microarrays consist of DNA microscopic points that are attached to a solid surface such as glass, plastic or silicon chip and formed as an array. The fixed pieces of DNA are considered as searchers. In an experiment, we can use thousands of searchers. Therefore, any microarray consists of the same number of genetic tests as the experiment performed on all of them in parallel. Whit this ability, arrays have speeded up the biological investigations. Microarray technology can be seen as a continued development of southern blotting. However, the most important stage in this technology, analysis of data, requires reliable bioinformatics tools achieving high reliabilities. Infectious diseases,...
An Effective Nonsmooth Optimization Algorithm for Locally Lipschitz Functions
, Article Journal of Optimization Theory and Applications ; Volume 155, Issue 1 , 2012 , Pages 180-195 ; 00223239 (ISSN) ; Yousefpour, R ; Sharif University of Technology
2012
Abstract
To construct an effective minimization algorithm for locally Lipschitz functions, we show how to compute a descent direction satisfying Armijo's condition. We present a finitely terminating algorithm to construct an approximating set for the Goldstein subdifferential leading to the desired descent direction. Using this direction, we propose a minimization algorithm for locally Lipschitz functions and prove its convergence. Finally, we implement our algorithm with matrix laboratory (MATLAB) codes and report our testing results. The comparative numerical results attest to the efficiency of the proposed algorithm
An effective optimization algorithm for locally nonconvex lipschitz functions based on mollifier subgradients
, Article Bulletin of the Iranian Mathematical Society ; Volume 37, Issue 1 , 2011 , Pages 171-198 ; 10186301 (ISSN) ; Yousefpour, R ; Sharif University of Technology
2011
Abstract
We present an effective algorithm for minimization of locally nonconvex Lipschitz functions based on mollifier functions approximating the Clarke generalized gradient. To this aim, first we approximate the Clarke generalized gradient by mollifier subgradients. To construct this approximation, we use a set of averaged functions gradients. Then, we show that the convex hull of this set serves as a good approximation for the Clarke generalized gradient. Using this approximation of the Clarke generalized gradient, we establish an algorithm for minimization of locally Lipschitz functions. Based on mollifier subgradient approximation, we propose a dynamic algorithm for finding a direction...
An adaptive competitive penalty method for nonsmooth constrained optimization
, Article Numerical Algorithms ; 2016 , Pages 1-32 ; 10171398 (ISSN) ; Shaeiri, M ; Sharif University of Technology
Springer New York LLC
2016
Abstract
We present a competitive algorithm to minimize a locally Lipschitz function constrained with locally Lipschitz constraints. The approach is to use an ℓ1 nonsmooth penalty function. The method generates second order descent directions to minimize the ℓ1 penalty function. We introduce a new criterion to decide upon acceptability of a Goldstein subdifferential approximation. We show that the new criterion leads to an improvement of the Goldstein subdifferential approximation, as introduced by Mahdavi-Amiri and Yousefpour. Also, making use of our proposed line search strategy, the method always moves on differentiable points. Furthermore, the method has an adaptive behaviour in the sense that,...
Constructing a sequence of discrete Hessian matrices of an SC1 function uniformly convergent to the generalized Hessian matrix
, Article Mathematical Programming ; Volume 121, Issue 2 , 2010 , Pages 387-414 ; 00255610 (ISSN) ; Yousefpour, R ; Sharif University of Technology
2010
Abstract
We construct a uniform approximation for generalized Hessian matrix of an SC1 function. Using the discrete gradient and the extended second order derivative, we define the discrete Hessian matrix.We construct a sequence of sets, where each set is composed of discrete Hessian matrices. We first show some new properties of SC1 functions. Then, we prove that for SC1 functions the sequence of the set of discrete Hessian matrices is uniformly convergent to the generalized Hessian matrix
An adaptive competitive penalty method for nonsmooth constrained optimization
, Article Numerical Algorithms ; Volume 75, Issue 1 , 2017 , Pages 305-336 ; 10171398 (ISSN) ; Shaeiri, M ; Sharif University of Technology
Springer New York LLC
2017
Abstract
We present a competitive algorithm to minimize a locally Lipschitz function constrained with locally Lipschitz constraints. The approach is to use an ℓ1 nonsmooth penalty function. The method generates second order descent directions to minimize the ℓ1 penalty function. We introduce a new criterion to decide upon acceptability of a Goldstein subdifferential approximation. We show that the new criterion leads to an improvement of the Goldstein subdifferential approximation, as introduced by Mahdavi-Amiri and Yousefpour. Also, making use of our proposed line search strategy, the method always moves on differentiable points. Furthermore, the method has an adaptive behaviour in the sense that,...
A conjugate gradient sampling method for nonsmooth optimization
, Article 4OR ; Volume 18, Issue 1 , May , 2020 , Pages 73-90 ; Shaeiri, M ; Sharif University of Technology
Springer
2020
Abstract
We present an algorithm for minimizing locally Lipschitz functions being continuously differentiable in an open dense subset of Rn. The function may be nonsmooth and/or nonconvex. The method makes use of a gradient sampling method along with a conjugate gradient scheme. To find search directions, we make use of a sequence of positive definite approximate Hessians based on conjugate gradient matrices. The algorithm benefits from a restart procedure to improve upon poor search directions or to make sure that the approximate Hessians remain bounded. The global convergence of the algorithm is established. An implementation of the algorithm is executed on a collection of well-known test problems....
A general fuzzy TOPSIS model in multiple criteria decision making
, Article International Journal of Advanced Manufacturing Technology ; Volume 45, Issue 3-4 , 2009 , Pages 406-420 ; 02683768 (ISSN) ; Heidarzade, A ; Sadeghpour Gildeh, B ; Mahdavi Amiri, N ; Sharif University of Technology
2009
Abstract
Decision making is the process of finding the best option among the feasible alternatives. In classical multiple criteria decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. Owning to vagueness of the decision data, the crisp data are inadequate for real-life situations. Since human judgments including preferences are often vague and cannot be expressed by exact numerical values, the application of fuzzy concepts in decision making is deemed to be relevant. In this paper, we proposed the application of a fuzzy distance formula in order to compute a crisp value for the standard deviation of fuzzy data. Then, we use this crisp value of the standard...
A comprehensive fuzzy TOPSIS model in multiple criteria decision making
, Article 37th International Conference on Computers and Industrial Engineering 2007, Alexandria, 20 October 2007 through 23 October 2007 ; Volume 1 , 2007 , Pages 537-548 ; 9781627486811 (ISBN) ; Sadeghpour Gildeh, B ; Heidarzade, A ; Mahdavi Amiri, N ; Sharif University of Technology
2007
Abstract
Decision making is the process of finding the best option among the feasible alternatives. In classical multiple criteria decision making (MCDM) methods, the ratings and the weights of the criteria are known precisely. Owning to vagueness of the decision data, the crisp data are inadequate for real-life situations. Since human judgments including preferences are often vague and cannot be expressed by exact numerical values, the application of fuzzy concepts in decision making is deemed to be relevant. Here, we apply a new distance formula to compute the standard deviation of fuzzy data to achieve crisp data by normalization. There is a flexibility to consider various fuzzy values as...
Diagrammatic approach for constructing multiresolution of primal subdivisions
, Article Computer Aided Geometric Design ; Volume 51 , 2017 , Pages 4-29 ; 01678396 (ISSN) ; Mahdavi Amiri, A ; Samavati, F ; Mahdavi Amiri, N ; Sharif University of Technology
Elsevier B.V
2017
Abstract
It is possible to define multiresolution by reversing the process of subdivision. One approach to reverse a subdivision scheme appropriates pure numerical algebraic relations for subdivision using the interaction of diagrams (Bartels and Samavati, 2011; Samavati and Bartels, 2006). However, certain assumptions carried through the available work, two of which we wish to challenge: (1) the construction of multiresolutions for irregular meshes are reconsidered in the presence of any extraordinary vertex rather than being prepared beforehand as simple available relations and (2) the connectivity graph of the coarse mesh would have to be a subgraph of the connectivity graph of the fine mesh. 3...
Genetic algorithm for solving fuzzy shortest path problem in a network with mixed fuzzy arc lengths
, Article AIP Conference Proceedings, 2 December 2010 through 4 December 2010, Sarawak ; Volume 1337 , 2011 , Pages 265-270 ; 0094243X (ISSN) ; 9780735408937 (ISBN) ; Tajdin, A ; Hassanzadeh, R ; Mahdavi-Amiri, N ; Shafieian, H ; Sharif University of Technology
2011
Abstract
We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α -cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers. we propose a new approach to solve the fuzzy APSPP using of genetic algorithm. Examples are worked out to illustrate the applicability of the proposed model