Sharif Digital Repository

In the new environment of power system, generating units dispatch problem plays an important role inasmuch as it is used to clear power market transactions. That is, market operator solves an optimization problem in order to find the energy and capacity reserve quota of each generating units participating in the power market. There are different techniques for solving this problem which differ in many aspects, including the objective function, optimization algorithm, and feasibility, so using an efficient technique is vital. In the real competitive electricity market, the components of generating units dispatch problem are faced with the uncertainties; therefore, due to the presence of... 

With the advent of small-scale heat and electricity producers in distribution energy systems, the interdependencies between energy carriers have been increased. Moreover, the rapid deployment of micro CHP, electric heat pumps, electricity-to-heat appliances etc., calls for new local market frameworks to be employed in distribution energy systems. In response, this paper presents a new energy market framework based on the concept of peer-to-peer negotiations to facilitate energy transactions between agents at the distribution level while addressing the interdependencies between different energy carriers. Moreover, linear optimization problems are proposed to investigate the optimal strategies... 

Recently, distance metric learning has been received an increasing attention and found as a powerful approach for semi-supervised learning tasks. In the last few years, several methods have been proposed for metric learning when must-link and/or cannot-link constraints as supervisory information are available. Although many of these methods learn global Mahalanobis metrics, some recently introduced methods have tried to learn more flexible distance metrics using a kernel-based approach. In this paper, we consider the problem of kernel learning from both pairwise constraints and unlabeled data. We propose a method that adapts a flexible distance metric via learning a nonparametric kernel... 

We consider a class of optimization problems called movement minimization on euclidean plane. Given a set of m nodes on the plane, the aim is to achieve some specific property by minimum movement of the nodes. We consider two specific properties, namely the connectivity (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 extend some known result on graphical grounds and obtain inapproximability results on the geometrical grounds. For the... 

Facility layout problem is a well-known optimization problem which generally deals with the arrangement of the facilities required in an organization. This problem has received much attention during the past decades. However, the researchers have mainly focused on the case where a single floor is available. While, in the competitive world, it is clear that using multi-floor structure layouts are much more efficient and in some cases necessary due to the nature of the functions and activities performed in the firms. Hence, this issue has recently attracted much attention and is becoming increasingly popular. Nevertheless, the lack of a review study on this subject for directing the new... 

This paper presents a new approach to economic dispatch (ED) problem with non-continuous and non-smooth cost functions using a hybrid evolutionary programming (EP) algorithm. In the proposed method the concept of multi-agent (MA) systems and EP are integrated together to form a new multi-agent evolutionary programming (MAEP) approach. In MAEP, an agent represents a candidate solution to the optimization problem in hand, and all agents live together in a global environment. Each agent senses its local environment, competes with its neighbors, and also learns by using its own knowledge. MAEP uses these agent-agent interactions and the evolutionary mechanism of EP to obtain the optimal... 

In this paper, a new approach based on hybrid Particle Swarm-Based- Simulated Annealing Optimization (PSO-B-SA) for solving thermal unit commitment (UC) problems is proposed. The PSO-B-SA presented in this paper solves the two sub-problems simultaneously and independently; unit-scheduled problem that determines on/off status of units and the economic dispatch problem for production amount of generating units. Problem formulation of UC is defined as minimization of total objective function while satisfying all the associated constraints such as minimum up and down time, production limits and the required demand and spinning reserve. Simulation results show that the proposed approach can... 

In this paper, a new approach for online reactive path generation and control of multi-agent systems is proposed. This method is based on local optimization techniques used for solving the inverse kinematic problem of redundant manipulators. Convergence of the agents' velocities to the desired values in the null-space of the primary task is guaranteed by introducing a new control law. The efficacy of the proposed algorithm is demonstrated through simulation experiments. © 2006 IEEE  

Distinguishing reflected waveforms from two separated targets which are very close to each other is an important challenge in radar signal processing. Pulse compression is a technique used for accounting for this problem. There are several methods for compressing such as phase coding waveform and the goal of this paper is finding these optimal codes. In this paper, by combining several contents, a new optimum method based on Genetic Algorithm is suggested. This method has low computational operation and its speed is faster than the other ordinary algorithms. This method is belonged to local or partial search methods and has following advantages: 1. It uses branch-and-bound strategy; 2. It's... 

In this paper, we study an adaptive random search method based on continuous action-set learning automaton for solving stochastic optimization problems in which only the noise-corrupted value of function at any chosen point in the parameter space is available. We first introduce a new continuous action-set learning automaton (CALA) and study its convergence properties. Then we give an algorithm for optimizing an unknown function. © 2005 The Franklin Institute. Published by Elsevier Ltd. All rights reserved  

In this paper, an adaptive random search method, based on continuous action-set learning automata, is studied for solving stochastic optimization problems in which only the noisecorrupted value of a function at any chosen point in the parameter space is available. First, a new continuous action-set learning automaton is introduced and its convergence properties are studied. Then, applications of this new continuous action-set learning automata to the minimization of a penalized Shubert function and pattern classification are presented. © Sharif University of Technology  

In this paper a new approach to dynamics optimization of rough terrain rovers is introduced. Since rover wheels traction has a significant role in rover mobility, optimization is based on the minimization of traction at rover wheel-ground interfaces. The method of optimization chosen is Genetic Algorithm (GA) which is a directed random search technique along with the usual optimization based on directional derivatives. GA is a suitable and efficient method of optimization for nonlinear problems. The procedure is applied on a specific rough terrain rover called CEDRA-I Shrimp Rover. Dynamical equations are obtained using Kane's method. Finally, the results are verified by modeling of the... 

We propose an algorithm based on mutual information to address the problem of online reconstruction of dynamic MRI from partial k-space measurements. Most of previous compressed sensing (CS) based methods successfully leverage sparsity constraint for offline reconstruction of MR images, yet they are not used in online applications due to their complexities. In this paper, we formulate the reconstruction as a constraint optimization problem and try to maximize the mutual information between the current and the previous time frames. Conjugate gradient method is used to solve the optimization problem. Using Cartesian mask to undersample k-space measurements, the proposed method reduces... 

Nature, as a rich source of solutions, can be an inspirational guide to answer scientific expectations. Seed dispersal mechanism as one of the most common reproduction method among the plants is a unique technique with millions of years of evolutionary history. In this paper, inspired by plants survival, a novel method of optimization is presented, which is called Fertile Field Algorithm. One of the main challenges of stochastic optimization methods is related to the efficiency of the searching process for finding the global optimal solution. Seeding procedure is the most common reproduction method among all the plants. In the proposed method, the searching process is carried out through a... 

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 this work, the wind farm layout optimization problem is dealt with using a new approach. The aim of wind farm layout optimization is to maximize the output power of a wind farm considering the wake losses. Layout optimization minimizes the wake losses regarding the location of the turbines. Three different wind scenarios with different wind direction angles, wind direction blowing probabilities, and Weibull distribution parameters are assumed. Since, the problem is nonlinear and constrained, imperialist competitive algorithm is used as a modern and powerful algorithm for continuous optimization problems. The optimization outcomes indicate that imperialist competitive algorithm yields... 

In this paper, three fundamental properties of a potential field-based flocking algorithm, i.e. merging of neighbouring graphs during the system evolution, collision avoidance and convergence of position of the centre of mass of informed agents to that of virtual leader are discussed. Next, these properties are utilized to determine required number of informed agents based on initial position of uninformed ones and consequently reduce the domain of search in optimization problems defined for finding the optimal number of required informed agents. Finally, a new optimization framework is proposed, which benefits Voronoi diagrams in order to reduce the number of informed agents required for... 

In this paper, we study the impact of sensing error and channel fading on the decision process of a multiple secondary user network in a primary network whose channel occupancy states are modelled as a Bernoulli process. We present a randomized access strategy to maximize total secondary network throughput. The proposed method guarantees that the probability of collision between primary and secondary users in each channel is less than the predefined value of P c = ξ. To find the optimal access strategy, we formulate secondary network throughput as an optimization problem. Then, using the KKT method to find the solution, we break the original problem into multiple sub-problems. Then, we... 

During the last decade, there has been a growing interest toward the problem of sparse decomposition. A very important task in this field is dictionary learning, which is designing a suitable dictionary that can sparsely represent a group of training signals. In most dictionary learning algorithms, the cost function to determine the the optimum dictionary is the ℓ0 norm of the matrix of decomposition coefficients of the training signals. However, we believe that this cost function fails to fully express the goal of dictionary learning, because it only sparsifies the whole set of coefficients for all training signals, rather than the coefficients for each training signal individually. Thus,...