Sharif Digital Repository

Integration of renewable energies in generation sector of power systems has caused many new issues for planners and decision makers. Therefore, new concepts such as flexibility are introduced to properly cover these problems. In this paper, a recently announced short-term flexibility product, namely, 'Flexible ramping product' (FRP) in power markets is put under investigation from different aspects. FRP is the capacity reserved to meet next upcoming five-minute net load (load minus intermittent supply) uncertainty. Therefore, at first, the mathematical model of real-time dispatch (RTD) problem in presence of the FRP is formulated. Modeling this problem as a linear programming (LP)... 

A novel neutron Spectrum Deconvolution using Particle Swarm Optimization (SDPSO) code has been developed to unfold the neutron spectrum from a pulse height distribution and a response matrix. The Particle Swarm Optimization (PSO) imitates the bird flocks social behavior to solve complex optimization problems. The results of the SDPSO code have been compared with those of the standard spectra and recently published Two-steps Genetic Algorithm Spectrum Unfolding (TGASU) code. The TGASU code have been previously compared with the other codes such as MAXED, GRAVEL, FERDOR and GAMCD and shown to be more accurate than the previous codes. The results of the SDPSO code have been demonstrated to... 

In this paper, an extension of the recently developed Crow Search Algorithm (CSA) to multi-objective optimization problems is presented. The proposed algorithm, called Multi-Objective Crow Search Algorithm (MOCSA), defines the fitness function using a set of determined weight vectors, employing the max-min strategy. In order to improve the efficiency of the search space, the performance space is regionalized using specific control points. A new chasing operator is also employed in order to improve the convergence process. Numerical results show that MOCSA is closely comparable to well-known multi-objective algorithms. © 2017 IEEE  

A heuristic predictive line-of-sight (LOS) guidance law is introduced to intercept a high-speed maneuvering target. A combination of continuous ant colony system and tabu search optimization algorithms is proposed to generate the optimal predictive commands of LOS guidance law. Prediction is driven by the previous positions of the target to estimate the next positions of it. Thus, the guidance system is continually solving a dynamic optimization problem in order to determine the acceleration commands by minimizing a cost function subject to actuators saturation. This innovation distinguishes the proposed guidance law from the classic LOS guidance, described by a simple relation between the... 

Modeling of hybrid systems using mixed logical dynamical (MLD) systems is an art. The MLD framework often introduces numerous constraints and auxiliary binary and continuous variables, which, in turn, increase the computational complexity of the optimization problems. This paper presents an automaton-based realization for discrete-time hybrid automaton (DHA) with both controlled and uncontrolled switching phenomena by which it is attempted to develop efficient translation techniques to MLD systems and reduce the total number of decision variables in the MLD model. Based on this DHA model, a modified version of MLD systems, which is called extended MLD (EMLD) is formally defined and... 

In this study, a new algorithm for explicit model predictive control of linear discrete-time systems subject to linear constraints, disturbances, uncertainties, and actuator faults is developed. The algorithm is based on dynamic programming, constraint rearrangement, multi-parametric programming, and a solution combination procedure. First of all, the dynamic programming is used to recast the problem as a multi-stage optimization problem. Afterwards, the constraints are rearranged in an innovative manner to take into account the worst admissible situation of unknown bounded disturbances, uncertainties, and actuator faults. Then, the explicit solution of the reformulated optimization problem... 

In this research, a new metaheuristic optimization algorithm, inspired by biological nervous systems and artificial neural networks (ANNs) is proposed for solving complex optimization problems. The proposed method, named as neural network algorithm (NNA), is developed based on the unique structure of ANNs. The NNA benefits from complicated structure of the ANNs and its operators in order to generate new candidate solutions. In terms of convergence proof, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of NNA is proved theoretically. In this paper, the NNA with its interconnected computing unit is examined... 

In conventional arrays, improving angular resolution requires larger aperture which demands more number of elements. On the other hand, array thinning is an efficient method of achieving narrow beamwidth (high angular resolution) with fewer number of elements. Reducing the number of elements results in reducing weight, cost, hardware complexity, and energy consumption. In this paper, a novel dynamic programming algorithm of array thinning with the objective of reducing sidelobe levels (SLLs), desired for large circular arrays, is proposed. The circular array is partitioned into annular rings, and the objective of the optimization problem is to determine the number of active elements in each... 

Residential energy management (REM) program is a demand response (DR) tool that automatically manages energy consumption of controllable household appliances to improve the energy consumption profile of a house according to electricity price. REM intends not only to improve technical aspects of distribution systems but also motivate customers for active participation in DR programs. In this regard, this paper proposes a two-level REM framework. In the first level, each customer runs an optimization problem to minimize his payment cost and sends the desired operation scheduling of appliances and the payment cost to the system operator. In the second level, a multiobjective (MO) optimization... 

This paper introduces a novel and practical vendor selection problem of a firm that cooperates with multiple geographically dispersed stores. In this problem, the firm entrusts some of its business process to external vendors, and each store can split the ordered quantity between one or more potential vendors, represented as a multi-sourcing strategies. Moreover, the Cobb–Douglas demand function is utilised to establish a relationship between the market demand and the selling price; representing price-sensitive demand. This paper seeks to choose the best vendors, to allocate the stores to them, and to find the optimal values for inventory-related decisions. The approach is based on the... 

In this paper, the agent velocity in robotic swarm was determined by using particle swarm optimization (PSO) to maximize the robotic swarm coordination velocity. A swarm as supposed here is homogenous and includes at least two members. Motion and behavior of swarm members are mostly result of two different phenomena: interactive mutual forces and influence of the agent. Interactive mutual forces comprise both attraction and repulsion. To be more realistic the field of the swarm members' view is not infinity. So influence of the coordinator agent on the robotic swarm would be local. The objective here is to guide the robotic swarm with maximum possible velocity. According to equation motion... 

In the last few years, we have witnessed an explosion in applications of sparse representation, the majority of which share the need for finding sparse solutions of underdetermined systems of linear equations (USLE's). Based on recently proposed smoothed ℓ0-norm (SL0), we develop a noise-tolerant algorithm for sparse representation, namely Robust-SL0, enjoying the same computational advantages of SL0, while demonstrating remarkable robustness against noise. The proposed algorithm is developed by adopting the corresponding optimization problem for noisy settings, followed by theoreticallyjustified approximation to reduce the complexity. Stability properties of Robust-SL0 are rigorously... 

Recently, Ghanbari and Mahdavi-Amiri (Appl Math Model 34:3363–3375, 2010) gave the general compromised solution of an LR fuzzy linear system using ABS algorithm. Here, using this general solution, we solve quadratic programming problems with fuzzy LR variables. We convert fuzzy quadratic programming problem to a crisp quadratic problem by using general solution of fuzzy linear system. By using this method, the crisp optimization problem has fewer variables in comparison with other methods, specially when rank of the coefficient matrix is full. Thus, solving the fuzzy quadratic programming problem by using our proposed method is computationally easier than the solving fuzzy quadratic... 

In this paper a new framework for scheduling of available resources in the distribution networks is developed. In this respect attempts are focused on interactions between charging/discharging profiles of electric vehicles (EVs) and output power of distributed generation units. To reach this goal, the proposed framework is designed as a two-stage optimization procedure. In the first stage, the charging/discharging schedules of EVs are extracted running a linear programing optimization problem taking into account the EV users' constraints and requirements. The usage profiles of the DG units, strategy of buying electricity from the market and also the final charging/discharging patterns of the... 

In this paper, we propose two methods for signal denoising where both signal and noise are sparse but in different domains. First, an optimization problem is proposed which is non-convex and NP-hard due to the existence of ℓ 0 norm in its cost function. Then, we propose two approaches to approximate and solve it. We also provide the proof of convergence for the proposed methods. The problem addressed in this paper arises in some applications for example in image denoising where the noise is sparse, signal reconstruction in the case of random sampling where the random mask is unknown, and error detection and error correction in the case of missing samples. The experimental results indicate... 

In this paper, we investigate the resource allocation in an underlay cognitive radio network when multiple primary and secondary users are served via multi-carrier non-orthogonal multiple access (MC-NOMA) scheme. Our main objective is to maximize the sum rate subject to a minimum guaranteed rate for the primary users. The performance of the system is highly affected by the power allocation and subchannel assignment for all users. Hence, our optimization problem is formulated as a nonconvex mixed integer non-linear program whose global optimum can be found through a computationally infeasible exhaustive search. To overcome this challenge, we propose an efficient iterative algorithm to jointly... 

As a promising solution for future indoor access networks, resource allocation in Visible light communication or LiFi networks is subject to lots of researches. An interesting approach for network algorithm design is to use some knowledge about the future of the network. With this regards, the anticipatory design may improve the performance of the system in terms of delay and throughput. This paper reviews the state-of-the-art anticipatory algorithms proposed in the literature, given different prediction capabilities. The key element that all of these algorithms share is to find an event that correlates the current actions to the performance of the network in the future. Apart from the... 

We study the problem of reconstructing a block-sparse signal from compressively sampled measurements. In certain applications, in addition to the inherent block-sparse structure of the signal, some prior information about the block support, i.e., blocks containing non-zero elements, might be available. Although many block-sparse recovery algorithms have been investigated in the Bayesian framework, it is still unclear how to incorporate the information about the probability of occurrence into regularization-based block-sparse recovery in an optimal sense. In this letter, we bridge between these fields by the aid of a new concept in conic integral geometry. Specifically, we solve a weighted... 

The desired milling process with high material removal rate (MRR) and low surface roughness of the product can be achieved only if machining chatter is absent. Incorporating chatter into the optimal selection of the machining parameters leads to a complex problem. Therefore, the approach of selecting conservative intervals for the machining parameters is usually employed instead. In this paper, a practical approach is proposed to specify the optimal machining parameters (depth of cut and spindle speed) in order to maximize MRR and minimize forced vibrations by considering machining chatter. Firstly, the worst-case scenario-based optimization problem in terms of the surface quality is solved... 

Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)...