Control and Optimization
Nader Kanzi
Volume 2, Issue 2 , December 2017, , Pages 33-44
Abstract
This paper proposes a new form of optimization problem which is a two-level programming problem with infinitely many lower level constraints. Firstly, we consider some lower level constraint qualifications (CQs) for this problem. Then, under these CQs, ...
Read More
This paper proposes a new form of optimization problem which is a two-level programming problem with infinitely many lower level constraints. Firstly, we consider some lower level constraint qualifications (CQs) for this problem. Then, under these CQs, we derive formula for estimating the subdifferential of its valued function. Finally, we present some necessary optimality conditions as Fritz-John type for the problem.
Control and Optimization
Zohreh Dadi; Farzaneh Ravanbakhsh
Volume 2, Issue 2 , December 2017, , Pages 45-60
Abstract
In this paper, a bidirectional ring network with three cells and different time delays is presented. To propose this model which is a good extension of three-unit neural networks, coupled cell network theory and neural network theory are applied. In this model, ...
Read More
In this paper, a bidirectional ring network with three cells and different time delays is presented. To propose this model which is a good extension of three-unit neural networks, coupled cell network theory and neural network theory are applied. In this model, every cell has self-connections without delay but different time delays are assumed in other connections. A suitable Lyapunov function is presented for this model which helps to get sufficient conditions to guarantee asymptotic and exponential stability of the model. Also, these conditions are independent of time delays. Finally, analytical results are confirmed by numerical examples which are stated.
Control and Optimization
Seyed Hamed Hashemi Mehne; Khodayar Javadi
Volume 2, Issue 2 , December 2017, , Pages 61-76
Abstract
A shape optimization problem of cooling fins for computer parts and integrated circuits is modeled and solved in this paper. The main purpose is to determine the shape of a two-dimensional pin fin, which leads to the maximum amount of removed heat. To do this, the shape optimization problem is defined ...
Read More
A shape optimization problem of cooling fins for computer parts and integrated circuits is modeled and solved in this paper. The main purpose is to determine the shape of a two-dimensional pin fin, which leads to the maximum amount of removed heat. To do this, the shape optimization problem is defined as maximizing the norm of the Nusselt number distribution at the boundary of the pin fin's connection profile. The governing differential equations are solved in solid and fluid phases separately. In order to formulate the optimization problem with finite dimensions, the shapes of the profiles are parameterized with cubic polynomials. Due to the lack of an explicit relation between the objective function and the geometric parameters, an approximate modeling method is used for the optimization process. The proposed method starts with three initial points. Then, the governing differential equations are solved for each of the profiles related to the initial points. The new step in this iterative process involves calculations based on a polynomial interpolation within the resulting Nusselt number norms. A numerical example is given to show the implementation and accuracy of the method.
Control and Optimization
Hassan Rashidi
Volume 2, Issue 2 , December 2017, , Pages 77-101
Abstract
The Minimum Cost Flow (MCF) problem is a well-known problem in the area of network optimisation. To tackle this problem, Network Simplex Algorithm (NSA) is the fastest solution method. NSA has three extensions, namely Network Simplex plus Algorithm (NSA+), Dynamic Network Simplex Algorithm (DNSA) and ...
Read More
The Minimum Cost Flow (MCF) problem is a well-known problem in the area of network optimisation. To tackle this problem, Network Simplex Algorithm (NSA) is the fastest solution method. NSA has three extensions, namely Network Simplex plus Algorithm (NSA+), Dynamic Network Simplex Algorithm (DNSA) and Dynamic Network Simplex plus Algorithm (DNSA+). The objectives of the research reported in this paper are to simulate and investigate the advantages and disadvantages of NSA compared with those of the three extensions in practical situations. To perform the evaluation, an application of these algorithms to scheduling problem of automated guided vehicles in container terminal is used. In the experiments, the number of iterations, CPU-time required to solve problems, overheads and complexity are considered.
Control and Optimization
Zahra Rafiei; Behzad Kafash; Seyyed Mehdi Karbassi
Volume 2, Issue 1 , April 2017, , Pages 1-13
Abstract
In order to obtain a solution to an optimal control problem, a numerical technique based on state-control parameterization method is presented. This method can be facilitated by the computation of performance index and state equation via approximating the control and state variable ...
Read More
In order to obtain a solution to an optimal control problem, a numerical technique based on state-control parameterization method is presented. This method can be facilitated by the computation of performance index and state equation via approximating the control and state variable as a function of time. Several numerical examples are presented to confirm the analytical findings and illustrate the efficiency of the proposed method.
Control and Optimization
Hadi Nasseri; Davood Darvishi Salokolaei; Allahbakhsh Yazdani
Volume 2, Issue 1 , April 2017, , Pages 15-28
Abstract
Linear assignment problem is one of the most important practical models in the literature of linear programming problems. Input data in the cost matrix of the linear assignment problem are not always crisp and sometimes in the practical situations is formulated by the grey systems theory approach. ...
Read More
Linear assignment problem is one of the most important practical models in the literature of linear programming problems. Input data in the cost matrix of the linear assignment problem are not always crisp and sometimes in the practical situations is formulated by the grey systems theory approach. In this way, some researchers have used a whitening technique to solve the grey assignment problem. Since the whitening technique only provides a crisp equivalent model and does not reflect the evolutionary characteristics of a grey set, it cannot keep the uncertainty properties in an interval involving the optimal solution. Based on these shortcomings, in this paper a new direct approach is introduced to solve linear assignment problem in grey environments. For preparing the mentioned method, some theoretical results are given to support the methodology. Finally, a numerical example will be solved to test the validity of the proposed method. Based on the suggested methodology, we emphasize that the same approach can be used whenever any linear programming model is formulated in grey environments.
Control and Optimization
Abbas Ali Rezaee; Farnoosh Zareian
Volume 2, Issue 1 , April 2017, , Pages 29-41
Abstract
In wireless sensor networks (WSNs), sensor nodes have limited resources with regard to computation, storage, communication bandwidth, and the most important of all, energy supply. In addition, in many applications of sensor networks, ...
Read More
In wireless sensor networks (WSNs), sensor nodes have limited resources with regard to computation, storage, communication bandwidth, and the most important of all, energy supply. In addition, in many applications of sensor networks, we need to send images to a sink node. Therefore, we have to use methods for sending images in which the number and volume of packets are optimized to save energy. Data compression is one of the optimization methods in energy consumption. In this paper, an effective compression algorithm is proposed to reduces computational and energy consumption and eventually, increases the overall network lifetime. Here in, we use a combination of three DCT, DWT and SWT wavelet transforms to achieve our goals. Simulation results show that the proposed algorithm achieves its goals with regard to data compression and reduction of energy consumption, and improves the network lifetime.
Control and Optimization
Azhdar Soleymanpour Bakefayat; Sima Karamseraji
Volume 2, Issue 1 , April 2017, , Pages 43-63
Abstract
The method of triangular functions (TF) could be a generalization form of the functions of block-pulse (Bp). The solution of second kind integral equations by using the concept of TF would lead to a nonlinear equations system. In this article, the obtained nonlinear system ...
Read More
The method of triangular functions (TF) could be a generalization form of the functions of block-pulse (Bp). The solution of second kind integral equations by using the concept of TF would lead to a nonlinear equations system. In this article, the obtained nonlinear system has been solved as a dynamical system. The solution of the obtained nonlinear system by the dynamical system through the Newton numerical method has got a particular priority, in that, in this method, the number of the unknowns could be more than the number of equations. Besides, the point of departure of the system could be an infeasible point. It has been proved that the obtained dynamical system is stable, and the response of this system can be achieved by using of the fourth order Runge-Kutta. The results of this method is comparable with the similar numerical methods; in most of the cases, the obtained results by the presented method are more efficient than those obtained by other numerical methods. The efficiency of the new method will be investigated through examples.
Control and Optimization
Alireza Fakharzadeh Jahromi; Zahra Alamdar Ghahferokhi
Volume 2, Issue 1 , April 2017, , Pages 65-76
Abstract
This paper describes a new optimization method for solving continuous semi-infinite linear problems. With regard to the dual properties, the problem is presented as a measure theoretical optimization problem, in which the existence of the solution is guaranteed. ...
Read More
This paper describes a new optimization method for solving continuous semi-infinite linear problems. With regard to the dual properties, the problem is presented as a measure theoretical optimization problem, in which the existence of the solution is guaranteed. Then, on the basis of the atomic measure properties, a computation method was presented for obtaining the near optimal solution by means of famous and simple simplex method. Some numerical results are reported to indicate the efficiency of the new method.
Control and Optimization
Seyed Mehdi Mirhosseini-Alizamini
Volume 2, Issue 1 , April 2017, , Pages 77-91
Abstract
The controlled harmonic oscillator with retarded damping, is an important class of optimal control problems which has an important role in oscillating phenomena in nonlinear engineering systems. In this paper, to solve this problem, we presented an analytical ...
Read More
The controlled harmonic oscillator with retarded damping, is an important class of optimal control problems which has an important role in oscillating phenomena in nonlinear engineering systems. In this paper, to solve this problem, we presented an analytical method. This approach is based on the homotopy perturbation method. The solution procedure becomes easier, simpler and more straightforward. In order to use the proposed method, a control design algorithm with low computational complexity is presented. Through the finite iterations of the proposed algorithm, a suboptimal control law is obtained for the problems. Finally, the obtained results have been compared with the exact solution of the controlled harmonic oscillator and variational iteration method, so that the high accuracy of the results is clear.
Control and Optimization
Hassan Zarei
Volume 1, Issue 2 , October 2016, , Pages 1-21
Abstract
In this paper, a computational approach is adopted for solving a multi-objective optimal control problem (MOOCP) formulation of optimal drug scheduling in human immunodeficiency (HIV) virus infected by individuals. The MOOCP, which uses a mathematical model of HIV infection, ...
Read More
In this paper, a computational approach is adopted for solving a multi-objective optimal control problem (MOOCP) formulation of optimal drug scheduling in human immunodeficiency (HIV) virus infected by individuals. The MOOCP, which uses a mathematical model of HIV infection, has some incompatible objectives. The objectives are maximizing the survival time of patients, the level of D4+ T-cells and the level of cytotoxic T-lymphocytes (CTLs), and minimizing the viral load and the drug costs. In this approach the fuzzy goals described by the linear membership functions, are incorporated for the objectives and the optimal solution is investigated by maximizing the degree of attainment of the aggregated fuzzy goals resulting a fuzzy goal optimal control problem (FGOCP). Using the minimum operator for aggregation of fuzzy goals, the FGOCP is converted into a constrained optimal control problem (OCP) in canonical form. The control parametrization enhancing technique (CPET) is used for approximating the OCP by an optimal parameter selection problem, with the final goal of implementing continuous and interrupted (structured treatment interruptions, STI) combinations of reverse transcriptase inhibitor (RTI) and protease inhibitor (PI) drug efficacies. Efficiency of the proposed method is confirmed by numerical simulations.
Control and Optimization
Ali Nehrani; Mohammad Keyanpour
Volume 1, Issue 2 , October 2016, , Pages 23-38
Abstract
In the present paper, optimal heating of temperature field which is modelled as a boundary optimal control problem, is investigated in the uncertain environments and then it is solved numerically. In physical modelling, a partial differential equation with ...
Read More
In the present paper, optimal heating of temperature field which is modelled as a boundary optimal control problem, is investigated in the uncertain environments and then it is solved numerically. In physical modelling, a partial differential equation with stochastic input and stochastic parameter are applied as the constraint of the optimal control problem. Controls are implemented as Dirichlet boundary conditions and representing the heating elements on the boundary of the field. In numerical quantification, stochastic input and parameter are approximated via Karhunen-Lo\'eve expansion and inserted to the problem. In fact, for numerical discretization of the problem stochastic Galerkin method is applied to generalize polynomial chaos. Numerical optimization is performed via gradient method. The problem is fully implemented and in order to show the applicability of the method, numerical examples are solved and numerical results are represented through figures.
Control and Optimization
Javad Mesbahi; Alaeddin Malek; Behnoush Salimbahrami
Volume 1, Issue 2 , October 2016, , Pages 39-52
Abstract
In this paper, the concept of synchronization control along with robust H∞ control are considered to evaluate the seismic response control on multi-story structures. To show the accuracy of the novel algorithm, a five-story structure is evaluated under the EL-Centro ...
Read More
In this paper, the concept of synchronization control along with robust H∞ control are considered to evaluate the seismic response control on multi-story structures. To show the accuracy of the novel algorithm, a five-story structure is evaluated under the EL-Centro earthquake load. In order to find the performance of the novel algorithm, random and uncertainty processes corresponding to Riccati equation is solved under a specific dynamic. Time history graphs corresponding to maximum displacement and floors force control are presented and evaluated. Despite the existence of random process and uncertainty in structure, stability and optimal performances are shown.
Control and Optimization
Mehdi Ahmadi; Hamid Esmaeili; R Erfanifar
Volume 1, Issue 2 , October 2016, , Pages 53-62
Abstract
In this paper, we suggest a fifth order convergence three-step method for solving system of nonlinear equations. Each iteration of the method requires two function evaluations, two first Fr\'{e}chet derivative evaluations and two matrix inversions. Hence, ...
Read More
In this paper, we suggest a fifth order convergence three-step method for solving system of nonlinear equations. Each iteration of the method requires two function evaluations, two first Fr\'{e}chet derivative evaluations and two matrix inversions. Hence, the efficiency index is $5^{1/({2n+4n^{2}+\frac{4}{3}n^{3}})}$, which is better than that of other three-step methods. The advantages of the method lie in the feature that this technique not only achieves an approximate solution with high accuracy, but also improves the calculation speed. Also, under several mild conditions the convergence analysis of the proposed method is provided. An efficient error estimation is presented for the approximate solution. Numerical examples are included to demonstrate the validity and applicability of the method and the comparisons are made with the existing results.
Control and Optimization
Rohollah Alesheykh
Volume 1, Issue 2 , October 2016, , Pages 63-75
Abstract
The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches. Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting ...
Read More
The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches. Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data. In this paper, a methodology has been employed to optimize the precision of defect detection of concrete slabs depending on their qualitative evaluation. Based on this idea, some machine learning algorithms such as C4.5 decision tree, RIPPER rule learning method and Bayesian network have been studied to explore the defect of concrete and to supply a decision system to speed up the defect detection process. The results from the examinations show that the proposed RIPPER rule learning algorithm in combination with Fourier Transform feature extraction method could get a defect detection rate of 93% as compared to other machine learning algorithms.
Control and Optimization
Siyamak Firouzian; Mohamad Adabitabar Firozja
Volume 1, Issue 2 , October 2016, , Pages 77-86
Abstract
Graph theory has an important role in the area of applications of networks and clustering. In the case of dealing with uncertain data, we must utilize ambiguous data such as fuzzy value, fuzzy interval value or values of fuzzy number. In this study, values ...
Read More
Graph theory has an important role in the area of applications of networks and clustering. In the case of dealing with uncertain data, we must utilize ambiguous data such as fuzzy value, fuzzy interval value or values of fuzzy number. In this study, values of fuzzy number were used. Initially, we utilized the fuzzy number value fuzzy relation and then proposed fuzzy number-value fuzzy graph on nodes and arcs. In this study, some properties of the graph on fuzzy number-value fuzzy graph were examined. First, we define the Cartesian product, composition, union and join operators on fuzzy number-value fuzzy graphs and then prove some of their properties and and give some examples for every one of definitions. We also introduced the notion of homomorphism, weak isomorphism,weak co-isomorphism, isomorphism, complete, weak complete and compliment on the fuzzy number fuzzy graphs and prove some of their properties and also present some examples for every one of them.
Control and Optimization
Akbar Hashemi Borzabadi; Manije Hasanabadi; Navid Sadjadi
Volume 1, Issue 1 , April 2016, , Pages 1-19
Abstract
In this paper an approach based on evolutionary algorithms to find Pareto optimal pair of state and control for multi-objective optimal control problems (MOOCP)'s is introduced. In this approach, first a discretized form of the time-control space is considered and then, a ...
Read More
In this paper an approach based on evolutionary algorithms to find Pareto optimal pair of state and control for multi-objective optimal control problems (MOOCP)'s is introduced. In this approach, first a discretized form of the time-control space is considered and then, a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using a numerical method. To do that, a modified version of two famous evolutionary genetic algorithm (GA) and particle swarm optimization (PSO) to obtain Pareto optimal solutions of the problem is employed. Numerical examples are presented to show the efficiency of the given approach.
Control and Optimization
Nader Kanzi
Volume 1, Issue 1 , April 2016, , Pages 21-30
Abstract
In this paper we study optimization problems with infinite many inequality constraints on a Banach space where the objective function and the binding constraints are locally Lipschitz. Necessary optimality conditions and regularity conditions are given. Our approach are based on the ...
Read More
In this paper we study optimization problems with infinite many inequality constraints on a Banach space where the objective function and the binding constraints are locally Lipschitz. Necessary optimality conditions and regularity conditions are given. Our approach are based on the Michel-Penot subdifferential.
Control and Optimization
Rasoul Hekmati
Volume 1, Issue 1 , April 2016, , Pages 31-40
Abstract
In this paper we run two important methods for solving some well-known problems and make a comparison on their performance and efficiency in solving nonlinear systems of equations. One of these methods is a non-monotone adaptive trust region strategy and another one is a scaled trust region ...
Read More
In this paper we run two important methods for solving some well-known problems and make a comparison on their performance and efficiency in solving nonlinear systems of equations. One of these methods is a non-monotone adaptive trust region strategy and another one is a scaled trust region approach. Each of methods showed fast convergence in special problems and slow convergence in other ones; we try to categorize these problems and find out that which method has better numerical behavior. The robustness of methods is demonstrated by numerical experiments.
Control and Optimization
Aghile Heydari; Hamid Reza Yousefzadeh
Volume 1, Issue 1 , April 2016, , Pages 41-53
Abstract
In this paper we try to introduce a new approach and study the notion of efficiency under a multi objectives linear programming problem in the university by using analysis of hierarchy process (AHP). To this end, we first extract some effective parameters due to efficiency offices ...
Read More
In this paper we try to introduce a new approach and study the notion of efficiency under a multi objectives linear programming problem in the university by using analysis of hierarchy process (AHP). To this end, we first extract some effective parameters due to efficiency offices in university and then prioritized these parameters by the AHP method. Hence, we could classify the most important factors of people's dissatisfaction in the offices and could underlie further studies in related offices to evaluate the efficiency and also effective factors for increasing the efficiency. More clearly, a mathematical model is suggested to calculate the amount of efficiency under a multi objectives linear programming problem and then it is solved by using the existing methods. Note that in order to examine the approach's performance, the Payame Noor University of Mashhad (PNUM) is selected as a case study. Numerical experiments are included to illustrate the effectiveness of the proposed approach.
Control and Optimization
Alaeddin Malek; Ghasem Ahmadi; Seyyed Mehdi Mirhoseini Alizamini
Volume 1, Issue 1 , April 2016, , Pages 55-67
Abstract
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization ...
Read More
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use a recurrent neural network model, with a simple structure based on a dynamical system to solve this problem. The portfolio selection problem and some other numerical examples are solved to evaluate the effectiveness of the presented model.
Control and Optimization
Aminalah Alba
Volume 1, Issue 1 , April 2016, , Pages 69-78
Abstract
Jahanshahloo has suggested a method for the solving linear programming problems with zero-one variables. In this paper we formulate fully fuzzy linear programming problems with zero-one variables and a method for solving these problems is presented using the ranking function and also the branch ...
Read More
Jahanshahloo has suggested a method for the solving linear programming problems with zero-one variables. In this paper we formulate fully fuzzy linear programming problems with zero-one variables and a method for solving these problems is presented using the ranking function and also the branch and bound method along with an example is presented.
