Sharif Digital Repository

Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the... 

The goal of this research is to formulate an inventory control problem of a single item with variable demand dependent on displayed stock level and selling price of the commodity. The item deteriorates based on a three-parameter Weibull distribution and advance payment is needed to purchase the item with the all-unit discount policy. Shortages are allowed partially and backlogged with the rate dependent on the length of customers' waiting time. The corresponding problem is formulated as a profit maximisation model. For solving this problem, four different variants of particle swarm optimisation (PSO) are utilised. Then, the application of the model is illustrated with the help of a numerical... 

Under voltage load shedding is one of the most important tools to avoid voltage instability. In this paper an optimal load shedding algorithm is developed. This approach is based on the concept of the static voltage stability margin and its sensitivity value at the maximum loading point or the collapse point. The traditional load shedding objective is extended to incorporate both technical and economical effects of load shedding. The voltage stability criterion is modeled as a soft constraint into load shedding scheme. The proposed methodology is implemented over the IEEE14 bus system and solved using a mathematical (GAMS/CONOPT) and two heuristic (P.S.O & GA) methods. © 2006 IEEE  

In this paper, a multiproduct multi-chance constraint stochastic inventory control problem is considered, in which the time-periods between two replenishments are assumed independent and identically distributed random variables. For the problem at hand, the decision variables are of integer-type, the service-level is a chance constraint for each product, and the space limitation is another constraint of the problem. Furthermore, shortages are allowed in the forms of fuzzy random quantities of lost sale that are backordered. The developed mathematical formulation of the problem is shown to be a fuzzy random integer-nonlinear programming model. The aim is to determine the maximum level of... 

Optimizing highway alignment requires a versatile set of cost functions and an efficient search method to achieve the best design. Because of numerous highway design considerations, this issue is classified as a constrained problem. Moreover, because of the infinite number of possible solutions for the problem and the continuous search space, highway alignment optimization is a complex problem. In this study, a customized particle swarm optimization algorithm was used to search for a near-optimal highway alignment, which is a compound of several tangents, consisting of circular (for horizontal design) and parabolic (for vertical alignment) curves. The selected highway alignment should meet... 

This paper presents a novel method fortuning the parameters of an sliding mode (SM) controller to obtain near-optimal performance. In order to do so the Linear Quadratic Regulator (LQR) was implemented on a linearized system. The input history of the LQR was used as a reference to obtain an optimal space for sliding mode controller parameters. Afterwards, the optimal space boundaries were dedicated to Genetic Algorithm (GA) to search for the optimal parameter for the nonlinear model. Also, the center of the obtained optimal space was used as an initial guess to the Particle Swarm Optimization (PSO) Algorithm. The proposed algorithm was implemented to regulate SM controller for the attitude... 

This paper shows a new possible way with particle swarm optimisation (PSO) to achieve an exergoeconomic optimisation of combinedcycle power plants. The optimisation has been done using a classic exergoeconomic and genetic algorithm, and the effects of using three methods are investigated and compared. The design data of an existing plant is used for the present analysis. Two different objective functions are proposed: One minimises the total cost of production per unit of output, and maximises the total exergetic efficiency. The analysis shows that the total cost of production per unit of output is 2%, 3%and 5% lower and exergy efficiency is 4%, 8% and 6% higher with respect to the base case... 

This paper proposes a particle swarm optimization based algorithm for locating series FACTS devices in deregulated electricity markets in order to reduce and manage congestion. Line outage sensitivity factors are used to reduce the solution space and to pinpoint the lines which are more suitable for FACTS device placement. Total congestion cost and total generation cost are the two objective functions that are employed in the optimization process. In order to verify and validate the effectiveness of the proposed method, it was applied to IEEE 14-bus and IEEE 57-bus test systems. The results obtained by the proposed method were compared with those of congestion rent contribution method  

In this study, multi-criteria shape optimization of an asymmetrical doublecurvature arch dam is presented. Simultaneous cost minimization of dam construction and maximum allowable tensile stress are investigated for an economical and safe design approach in the current study. Pareto front method was used to balance both the economy and safety of the design simultaneously, which can be difficult for both analysts and decision-makers. A non-dominated solution based on the important parameters of dam analysis and design is presented. To help decision-makers in their decision, two different methods are proposed. These methods for the case of an arch dam are Lombardi coefficient and equilibrium... 

This article explores the application of a wind farm layout optimization framework using a particle swarm optimizer to three benchmark test cases. The developed framework introduces an increased level of detail characterizing the impact that the wind farm layout can have on the levelized cost of energy by modelling the wind farm’s electrical infrastructure, annual energy production, and cost as functions of the wind farm layout. Using this framework, this paper explores the application of a particle swarm optimizer to the wind farm layout optimization problem considering three different levels of wind farm constraint faced by modern wind farm developers. The particle swarm optimizer is found... 

Perturbed-Chain Statistical Associating Fluid Theory Equation of State (PC-SAFT EoS) requires cross interaction parameter for each binary pair in the mixture. For real mixtures, these parameters should be corrected by binary interaction coefficients (kij's). The values of kij's are tuned by an optimization method in order to minimize the deviation from equilibrium data. The Particle Swarm Optimization (PSO) algorithm is employed for optimization of kij's due to the continuous nature of kij and highly nonlinear nature of PC-SAFT EoS. Although kij can be adjusted using the mentioned algorithm, it is cumbersome and highly time-consuming because the optimization should be performed for each pair... 

One of the most important challenges in target parameters estimation via model-free methods is selection of the most effective input parameters namely features selection (FS). Indeed, irrelevant features can degrade the estimation performance. In the current study, the challenge of choosing among the several plant parameters is tackled by means of the innovative FS algorithm named ranking of features with minimum deviation from the target parameter (RFMD). The selected features accompanied with the stable and the fast learning algorithm of multilayer perceptron (MLP) neural network (i.e. Levenberg-Marquardt algorithm) which is a combination of gradient descent and Gauss-newton learning... 

Increasing efficiency, improving energy consumption, and optimizing energy in industries are more than ever considered by researchers. Some methods such as nanoparticles use and porous medium are used to increase the heat transfer rate. For this reason, in this paper, simulation and optimization of a two-dimensional tube with the presence of water–silver nanofluid and porous media have been performed to improve heat transfer. Different profiles of the rate, pressure, and temperature of the two-dimensional tube at volume fraction, porosity coefficient and Darcy numbers have been obtained and finally, the results are compared. Then, the Nusselt number and the friction coefficient in the range... 

This paper is concerned about the optimal placement of phasor measurement units (PMUs) so as to make a system completely observable. Observability assessment is done by the aid of the topological observability rules. Moreover a new rule is added which can decrease the number of required PMUs for complete system observability. A modified binary particle swarm is used as an optimization tool for obtaining the minimal number of PMUs and corresponding configuration. In order to improve the speed of convergence, an initial PMU placement is provided by graph-theoretic procedure. The simulation results of proposed approach are presented for several IEEE test systems  

Intensity-modulated radiation therapy is a well-known technique for treating cancer patients worldwide. A treatment plan in this technique requires decision-making for three main problems: selection of beam angles, intensity map calculation, and leaf sequencing. Previous works investigated these problems sequentially. We present a new integrated framework for simultaneous decision-making of directions, intensities, and aperture shape, called direct angle and aperture optimization, and develop a mixed-integer nonlinear mathematical model for the problem. Due to the nonlinearity and the dimension of the problem, three efficient metaheuristics based on differential evolution (DE) called classic... 

The trade-off between the fuel consumption and drag coefficient makes the investigations of drag reduction of utmost importance. In this paper, the rear-end shape optimization of Ahmed body is performed. Before changing the geometry, to identify the suitable simulation method and validate it, the standard Ahmed body is simulated using k − ω shear stress transport (SST) and k-epsilon turbulence models. The slant angle, rear box angle, and rear box length as variables were optimized simultaneously. Optimizations conducted by genetic algorithm (GA) and particle swarm optimization (PSO) methods indicate a 26.3% decrease in the drag coefficient. To ensure the validity of the results, a... 

In this paper, a multi-product multi-chance constraint joint single-vendor multi-buyers inventory problem is considered in which the demand follows a uniform distribution, the lead-time is assumed to vary linearly with respect to the lot size, and the shortage in combination of backorder and lost-sale is assumed. Furthermore, the orders are placed in multiple of packets, there is a limited space available for the vendor, there are chance constraints on the vendor service rate to supply the products, and there is a limited budget for each buyer to purchase the products. While the elements of the buyers' cost function are holding, shortage, order and transportation costs, the set up and... 

New chemical process design strategies utilizing computer-aided molecular design (CAMD) can provide significant improvements in process safety by designing chemicals with required target properties and the substitution of safer chemicals. An important aspect of this methodology concerns the prediction of properties given the molecular structure. This study utilizes one such emerging method for prediction of a hazardous property, flash point (FP), which is in the center of attention in safety studies. Using such a reliable data set comprising 1651 organic and inorganic chemicals, from 79 diverse material classes, and robust dynamic binary particle swarm optimization for the feature selection... 

This contribution focuses on the use of ladder particle swarm optimisation (LPSO) on modelling of oxadiazole- and triazolesubstituted naphthyridines as human immunodeficiency virus-1 integrase inhibitors. Artificial neural network (ANN) and Monte Carlo cross-validation techniques were combined with LPSO to develop a quantitative structure-activity relationship model. The techniques of LPSO, ANN and sample set partitioning based on joint x-y distances were applied as feature selection, mapping and model evaluation, respectively. The variables selected by LPSO were used as inputs of Bayesian regularisation ANN. The statistical parameters of correlation of deterministic, R2, and... 

A novel bi-objective three-echelon supply chain problem is formulated in this paper in which cross-dock facilities to transport the products are modeled as an M/M/m queuing system. The proposed model is validated using the epsilon constraint method when applied to solve some small-size problems. Since the problem belongs to the class of NP-hard and that it is of a bi-objective type, a multi-objective particle swarm optimization (MOPSO) algorithm with a new solution structure that satisfies all of the constraints is developed to find Pareto solutions. As there is no benchmark available in literature, three other multi-objective meta-heuristic algorithms called non-dominated ranking genetic...