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, ...
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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
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 ...
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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.
Leader Navaei; Reza Akbari
Abstract
In this paper, the problem of identification of distributions for two independent objects via simple homogeneous stationary Markov chains with a finite number of states is studied. This problem is introduced by Ahlswede and Haroutunian on the identification of hypotheses under reliability ...
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In this paper, the problem of identification of distributions for two independent objects via simple homogeneous stationary Markov chains with a finite number of states is studied. This problem is introduced by Ahlswede and Haroutunian on the identification of hypotheses under reliability requirements. The problem of identification of distributions for one object via Markov chains was studied by Haroutunian and Navaei in 2009.
Razieh Farokhzad Rostami
Abstract
Fixed point theorems can be used to prove the solvability of optimization problems, differential equations and equilibrium problems, and the intrinsic flexibility of probabilistic metric spaces makes it possible to extend the idea of contraction mapping in several inequivalent ways. ...
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Fixed point theorems can be used to prove the solvability of optimization problems, differential equations and equilibrium problems, and the intrinsic flexibility of probabilistic metric spaces makes it possible to extend the idea of contraction mapping in several inequivalent ways. In this paper, we extend very recent fixed point theorems in the setting of Menger probabilistic metric spaces. We present some fixed point theorems for self-mappings satisfying a generalized (ϕ , ψ ) - contractive condition in Menger probabilistic metric spaces which are contractions used extensively in global optimization problems. On the other hand, we consider a more general class of auxiliary functions in the contractivity condition and prove the existence of fixed points of non-expansive mappings on Menger probabilistic metric spaces.
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, ...
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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.
Mohammad Mohammadi Najafabadi; Habibeh Nazif; Fahime Soltanian
Abstract
This paper is motivated by high dose rate brachytherapy treatment planning problems which involve the specification of the movement schedule of a radiation source so that the target volumes are adequately covered with sufficient doses and organs at risk are not radiated beyond the clinical acceptance ...
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This paper is motivated by high dose rate brachytherapy treatment planning problems which involve the specification of the movement schedule of a radiation source so that the target volumes are adequately covered with sufficient doses and organs at risk are not radiated beyond the clinical acceptance threshold. It utilizes four powerful multi-objective evolutionary algorithms (MOEA), which create a set of equally-weighted Pareto optimal solutions instead of only one and produce better results compared to other optimization methods. These algorithms include non-dominated sorting genetic algorithms, Pareto envelope-based selection algorithm, non-dominated ranking genetic algorithm, and strength Pareto evolutionary algorithm. The results indicate that the last algorithm uses the dependency between decision variables to solve them efficiently and is the best type of MOEA both in terms of convergence criteria and solution diversity maintenance for the brachytherapy problems.
Hadis Ahmadian Yazdi; Seyed Javad Seyyed Mahdavi Chabok; Maryam KheirAbadi
Abstract
In recent decades, the amount and variety of data have grown rapidly. As a result, data storage, compression, and analysis have become critical subjects in data mining and machine learning. It is essential to achieve accurate compression without losing important data in the process. Therefore, this ...
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In recent decades, the amount and variety of data have grown rapidly. As a result, data storage, compression, and analysis have become critical subjects in data mining and machine learning. It is essential to achieve accurate compression without losing important data in the process. Therefore, this work proposes an effective data compression method for recommender systems based on the attention mechanism. The proposed method performs data compression on two levels: features and records. It is time-aware and based on time windows, taking into account users' activity and preventing the loss of important data. The resulting technique can be efficiently utilized for deep networks, where the amount of data is a significant challenge. Experimental results demonstrate that this technique not only reduces the amount of data and processing time but also achieves acceptable accuracy.
Seyed Hossein Seyed Ebrahimi; Kambiz Majidzadeh; Farhad Soleimanian Gharehchopogh
Abstract
Classification is a crucial process in data mining, data science, machine learning, and the applications of natural language processing. Classification methods distinguish the correlation between the data and the output classes. In single-label classification ...
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Classification is a crucial process in data mining, data science, machine learning, and the applications of natural language processing. Classification methods distinguish the correlation between the data and the output classes. In single-label classification (SLC), each input sample is associated with only one class label. In certain real-world applications, data instances may be assigned to more than one class. The type of classification which is required in such applications is known as multi-label classification (MLC). In MLC, each sample of data is associated with a set of labels. Due to the presence of multiple class labels, the SLC learning process is not applicable to MLC tasks. Many solutions to the multi-label classification problem have been proposed, including BR, FS-DR, and LLSF. But, these methods are not as accurate as they could be. In this paper, a new multi-label classification method is proposed based on graph representation. A feature selection technique and the Q-learning method are employed to increase the accuracy of the proposed algorithm. The proposed multi-label classification algorithm is applied to various standard multi-label datasets. The results are compared with state-of-the-art algorithms based on the well-known performance evaluation metrics. Experimental results demonstrated the effectiveness of the proposed model and its superiority over the other methods.
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 ...
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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
Alireza Ahangarani Farahani; Abbas Dideban
Abstract
The existing modeling methods using Petri Nets, have been successfully applied to model and analyze dynamic systems. However, these methods are not capable of modeling all dynamic systems such as systems with the current sample time signals, systems including various ...
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The existing modeling methods using Petri Nets, have been successfully applied to model and analyze dynamic systems. However, these methods are not capable of modeling all dynamic systems such as systems with the current sample time signals, systems including various subsystems and multi-mode systems. This paper proposes Hybrid Time Delay Petri Nets (HTDPN) to solve the problem. In this approach, discrete and continuous Petri Nets are combined so that the continuous PNs part and the discrete PNs are responsible for past time samples and current sample time, respectively. To evaluate the performance of the proposed tool, it is employed to model a legless piezoelectric capsubot robot as a multi modes system and a $PID$ controller, in which the gains tuned by the Genetic Algorithm are designed for the resulting model by HTDPN. Results show that the proposed method is faster in terms of mathematical calculations which can reduce the simulation time and complexity of complicated systems. It would be observed that the proposed approach makes the $PID$ controller design simpler as well. In addition, a comparative study of capsubot has been performed. Simulation results show that the presented method is encouraging compared to the predictive control, which is used in the literature.
Mahdi Ahmadnia; Reza Ghanbari; Khatere Ghorbani-Moghadam
Abstract
In a water distribution network, in order to analyze and determine its parameters such as head and flow rate, we have to solve nonlinear hydraulic equations in each component of the network. Contrary to most of the water distribution network simulation software, solving ...
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In a water distribution network, in order to analyze and determine its parameters such as head and flow rate, we have to solve nonlinear hydraulic equations in each component of the network. Contrary to most of the water distribution network simulation software, solving these equations by using the gradient method, we propose a trust-region method to solve them, as the trust-region method is newer than the gradient method and has well worked in mathematical problems. To prove the effectiveness of our method, we made a comparison between our proposed method and the well-known gradient method. The results show that the trust-region method is convergent in all instances, but the gradient method diverges when the dimension of nonlinear hydraulic equations of water distribution networks increases. In addition, our results convince the solution obtained from the trust-region method is more accurate compared to the gradient method. Thus, using the trust-region method in solving the network equations can lead to a better hydraulic analysis of the network.
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 ...
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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
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 ...
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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.
Ali Badie; Mohammad Amin Moragheb; Ali Noshad
Abstract
This research explores the prominent signals and presents an effective approach to identify emotional experiences and mental states based on EEG signals. First, PCA is used to reduce the data's dimensionality from 2K and 1K down to 10 and 15 while improving the performance. Then, ...
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This research explores the prominent signals and presents an effective approach to identify emotional experiences and mental states based on EEG signals. First, PCA is used to reduce the data's dimensionality from 2K and 1K down to 10 and 15 while improving the performance. Then, regarding the insufficient high-quality training data for building EEG-based recognition methods, a multi-generator conditional GAN is presented for the generation of high-quality artificial data that covers a more complete distribution of actual data by utilizing different generators. Finally, to perform classification, a new hybrid LSTM-SVM model is introduced. The proposed hybrid network attained overall accuracy of 99.43% in EEG emotion state classification and showed an outstanding performance in identifying the mental states with accuracy of 99.27%. The introduced approach successfully combines two prominent targets of machine learning: high accuracy and small feature size, and demonstrates a great potential to be utilized in future classification tasks.
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, ...
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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.
Javad Shaker Ardakani; shahriar Farahmand Rad; Nader Kanzi
Abstract
This paper studies the convex multiobjective optimization problem with vanishing constraints. We introduce a new constraint qualification for these problems, and then a necessary optimality condition for properly efficient solutions is presented. Finally by imposing some ...
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This paper studies the convex multiobjective optimization problem with vanishing constraints. We introduce a new constraint qualification for these problems, and then a necessary optimality condition for properly efficient solutions is presented. Finally by imposing some assumptions, we show that our necessary condition is also sufficient for proper efficiency. Our results are formulated in terms of convex subdifferential.
Hamed Soroush
Abstract
This paper addresses a non-smooth multi-objective semi-infinite programming problem that involves a feasible set defined by inequality constraints. Our focus is on introducing a new weak Slater constraint qualification and deriving the necessary and sufficient conditions for (weakly, properly) ...
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This paper addresses a non-smooth multi-objective semi-infinite programming problem that involves a feasible set defined by inequality constraints. Our focus is on introducing a new weak Slater constraint qualification and deriving the necessary and sufficient conditions for (weakly, properly) efficient solutions to the problem using (weak and strong) Karush-Kuhn-Tucker types. Additionally, we present two duals of the Mond-Weir type for the problem and provide (weak and strong) duality results for them. All of the results are given in terms of Clarke subdifferential.
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, ...
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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.
Hamidreza Ayoughi; Hossein Dehghani Poudeh; Abbas Raad; Davood Talebi
Abstract
In this paper, a stable multi-objective model of location, inventory, and supply chain routing is presented under conditions of uncertainty and using a passive defense approach. Parameters such as demand, cost of setting up the facility ...
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In this paper, a stable multi-objective model of location, inventory, and supply chain routing is presented under conditions of uncertainty and using a passive defense approach. Parameters such as demand, cost of setting up the facility and cost of maintaining inventory are considered uncertain and in the form of triangular fuzzy numbers. Also, in order to increase supply chain resilience, the characteristics and capabilities of passive defense in the supply chain, such as ``ready flow rate'', ``security of backup routes'', ``possibility of deployment of resources and equipment'', and ``the principle of dispersion for location'' are considered. Multipurpose, multipartite algorithms, based on the Pareto archive and genetic algorithm, are used to solve the model. The results of validation show that the proposed model is valid and feasible, and the proposed algorithm is also valid and converges to the optimal solution. Sample problems, in three groups of small, medium and large, are solved by two algorithms, and the results are compared based on quality, dispersion, uniformity and execution time. The results of this section show that in all cases, the multi-objective particle mass algorithm has a higher ability than the GA to produce solutions of higher quality and to explore and extract the scalable area of the solution. Also, the comparison of the execution times of the algorithms indicates that the multi-objective particle mass algorithm has a higher solution time.
Ebrahim Amini
Abstract
In this article, we offer an efficient method to find an approximate solution for quadratic optimal control problems. The approximate solution is offered in a finite series form in reproducing kernel space. The convergence of proposed method is analyzed under some hypotheses ...
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In this article, we offer an efficient method to find an approximate solution for quadratic optimal control problems. The approximate solution is offered in a finite series form in reproducing kernel space. The convergence of proposed method is analyzed under some hypotheses which provide the theoretical basis of the proposed method for solving quadratic optimal control problems. Furthermore, in this study, we investigate the application of the proposed method to obtain the solution of equations that have formally been solved using Pontryagin's maximum principle. Moreover, many different types of quadratic optimal control problems are considered prototype examples. The obtained results demonstrate that the proposed method is truly effective and convenient to obtain the analytic and approximate solutions of quadratic optimal control problems.
Rasool Hatamian Joghali
Abstract
In 2010, Alvarez et al. proposed an algorithm for morphological snakes that could detect objects whose edges consist of convex sets and polygonal edges. However, the algorithm may not detect the boundary well if the edges of an object contain a convex set or if there are several separated ...
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In 2010, Alvarez et al. proposed an algorithm for morphological snakes that could detect objects whose edges consist of convex sets and polygonal edges. However, the algorithm may not detect the boundary well if the edges of an object contain a convex set or if there are several separated objects in an image. In this paper, we present two optimal sub-algorithms that are modifications to the Alvarez et al. algorithm. Our algorithms provide optimal edge detection for images and we present examples to demonstrate their effectiveness.
Control and Optimization
Kamal Fallahi
Abstract
In this paper, we introduce a new type of graph contraction using a special class of functions and give a best proximity point theorem for this contraction in complete metric spaces endowed with a graph under two different conditions. We then support our main theorem by a non-trivial ...
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In this paper, we introduce a new type of graph contraction using a special class of functions and give a best proximity point theorem for this contraction in complete metric spaces endowed with a graph under two different conditions. We then support our main theorem by a non-trivial example and give some consequences of best proximity point of it for usual graphs.
Yousef Edrisi-Tabriz
Abstract
In this paper, we present a numerical method for solving the fractional optimal control problems in which fractional integral operational matrices of basic B-spline functions are used. In the proposed method, we use the Riemann-Liouville fractional integral. With ...
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In this paper, we present a numerical method for solving the fractional optimal control problems in which fractional integral operational matrices of basic B-spline functions are used. In the proposed method, we use the Riemann-Liouville fractional integral. With the help of the operational matrix of the fractional integral and the collocation method, we transform the fractional optimal control problem into a nonlinear programming problem and then solve it with an appropriate optimization algorithm. Compared to similar numerical techniques, our method has better accuracy and efficiency, and also it is easy to use. To provide a clear view of the applicability and efficiency of our numerical method, several illustrative examples are presented.
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 ...
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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.
Saeed Nezhadhosein; Sahar Mohammadkhan Sartip
Abstract
Following the setting of the Dai-Liao (DL) parameter in conjugate gradient (CG) methods, we introduce two new parameters based on the modified secant equation proposed by Li et al. (Comput. Optim. Appl. 202:523-539, 2007) with two approaches, ...
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Following the setting of the Dai-Liao (DL) parameter in conjugate gradient (CG) methods, we introduce two new parameters based on the modified secant equation proposed by Li et al. (Comput. Optim. Appl. 202:523-539, 2007) with two approaches, which use an extended new conjugacy condition. The first is based on a modified descent three-term search direction, as the descent Hestenes-Stiefel CG method. The second is based on the quasi-Newton (QN) approach. Global convergence of the proposed methods for uniformly convex functions and general functions is proved. Numerical experiments are done on a set of test functions of the CUTEr collection and the results are compared with some well-known methods.