Sharif Digital Repository

Feature relevance estimation is one of the most successful techniques used for improving the retrieval results of a content-based image retrieval (CBIR) system based on users' feedbacks. In this class of approaches, the weights of the feature elements (FEs) are adjusted based on the relevance feedbacks (RFs) given by the users to reduce the so-called semantic gap in the underlying CBIR system. An analytical approach is proposed in this paper to convert the users' feedbacks to the appropriate FE weights by solving a constrained optimization problem. Experiments on a set of 11,000 images from the Corel database show that the proposed approach outperforms other existing short-term RF approaches... 

Coupled continuous quadratic knapsack problems (CCK) are introduced in the present study. The solution of a CCK problem is equivalent to the projection of an arbitrary point onto the volume constrained Gibbs N-simplex, which has a wide range of applications in computational science and engineering. Three algorithms have been developed in the present study to solve large scale CCK problems. According to the numerical experiments of this study, the computational costs of presented algorithms scale linearly with the problem size, when it is sufficiently large. Moreover, they show competitive or even superior computational performance compared to the advanced QP solvers. The ease of... 

This paper aims to propose an approach based on NSGA-II for solving multi-objective non-linear fixed charge transportation problem with multiple modes of transport in crisp and interval environments. Certain modifications need to be made in the existing NSGA-II configuration to calculate the crowding distance of a solution in the interval environment. Besides, a crossover and a mutation scheme suitable for multiple modes of transportation are developed. In the end, a set of test problems are solved in both environments and some comparative studies are performed restricting the problem to only one mode of transport at a time. Finally, the results of the proposed algorithm are compared with... 

Short term hydrothermal scheduling (STHTS) is a non-linear, multi-modal and very complex constrained optimization problem which has been solved using several conventional and modern metaheuristic optimization algorithms. A number of research articles have been published addressing STHTS using different techniques. This article presents a comprehensive review of research published for solving the STHTS problem in the last four decades. © 2013 IEEE  

This article addresses a centralized generation expansion planning problem, accounting for both long- and short-term uncertainties. The long-term uncertainty (demand growth) is modeled via a set of scenarios, while the short-term uncertainty (wind power generation) is described by a family of probability distributions with the same first- and second-order moments obtained from historical data. The resulting model is a distributionally robust chance-constrained optimization problem, which selects the conventional generating units to be built among predefined discrete options. This model includes a detailed representation of unit commitment constraints. To achieve computational tractability,... 

A new computational algorithm is introduced in the present study to solve multimaterial topology optimization problems. It is based on the penalization of the objective functional by the multiphase volume constrained Ginzburg-Landau energy functional. The update procedure is based on the gradient flow of the objective functional by a fractional step projected steepest descent method. In the first step, the new design is found based on the projected steepest descent method to ensure the reduction in the objective functional, simultaneously satisfying the control constraints. In the second step, regularization step, an H1 regularity of the solution is ensured while keeping the feasibility of... 

Nature has always been a source of great inspiration for engineers and mathematicians. Evolutionary Algorithms are the latest in a line of natural-based innovations which have had a profound effect on the application of optimization in science and engineering. Although based on nature, Evolutionary Algorithms are nonetheless distinctly different from natural evolution in several areas. This paper outlines early and recent developments of Evolutionary Algorithms while covering those areas of difference. Practical issues related to the use of Evolutionary Algorithms, key parameters that affect the quality of the search and impact of user choices in problem formulation are also covered in this... 

A new method for structural optimization is presented for successive approximation of the objective function and constraints in conjunction with Lagrange multipliers approach. The focus is on presenting the methodology with simple examples. The basis of the iterative algorithm is that after each iteration, it brings the approximate location of the estimated minimum closer to the exact location, gradually. In other words, instead of the linear or parabolic term used in Taylor expansion, which works based on a short step length, an arch is used that has a constant curvature but a longer step length. Using this approximation, the equations of optimization involve the Lagrange multipliers as the... 

An algorithm is proposed to optimize the performance of multiphase structures (composites) under elastodynamic loading conditions. The goal is to determine the distribution of material in the structure such that the time-averaged total stored energy of structure is minimized. A penalization strategy is suggested to avoid the checkerboard instability, simultaneously to generate near 0-1 topologies. As a result of this strategy, the solutions of presented algorithm are sufficiently smooth and possess the regularity of H1 function space. A simple method for the continuum adjoint sensitivity analysis of the corresponding PDE-constrained optimization problem is presented. It is general and can be... 

We study the tradeoff between the sum rate and the error probability in downlink of wireless networks. Using the recent results on the achievable rates of finite-length codewords, the problem is cast as a joint optimization of the network sum rate and the per-user error probability. Moreover, we develop an efficient algorithm based on the divide-and-conquer technique to simultaneously maximize the network sum rate and minimize the maximum users’ error probability and to evaluate the effect of the codeword length on the system performance. The results show that, in delay-constrained scenarios, optimizing the per-user error probability plays a key role in achieving high throughput. IEEE  

In this paper, we show how time-sharing method may reduce transmission delay in the dynamic index coding scenario. We propose a novel time-shared dynamic index coding transmission scheme that achieves the maximum index coding gain for a complete bi-directional side information graph and formulate a constrained optimization problem to tune the transmission scheme for the minimum transmission delay. A closed-form solution is presented for the special case of two-user. We also use analytical and simulation results to provide graphical intuition for the obtained results  

This paper presents a practical reactive power valuation method, for the purpose of the reactive power pricing, in which the VAr value is decomposed into two components. These components are basically different from those already employed in the literature. Using a security constrained optimization model, the proposed scheme allocates two specific amounts of MW transactions to each generator as a dynamic reactive power source. One of these allocated amounts represents the contribution of the VAr resource to the shipment of active power, while the other one represents the contribution to the protection of the system against voltage instability. The final VAr value is derived using the... 

Link adaptation of a wireless cellular network is of much attention due to the desire of maximizing both the a erage lin throughput and co erage reliability, which have conflicting effect on each other. In this paper, the intra-cell lin adaptation problem is formulated as a non-differentiable constrained optimization problem to maximize average link throughput while guaranteeing the best possible coverage reliability. To achieve this, a cutting plane optimization algorithm is employed. The performance of our method: adaptive modulation/coding with power management (AMCWPM) using proposed algorithm is compared with that of an adaptive modulation/coding (AMC) with no power management....