Sharif Digital Repository

Bus terminal assignment with the objective of maximizing public transportation service is known as bus terminal location problem (BTLP). We formulate the BTLP, a problem of concern in transportation industry, as a p-uncapacitated facility location problem (p-UFLP) with distance constraint. The p-UFLP being NP-hard (Krarup and Pruzan, 1990), we propose evolutionary algorithms for its solution. According to the No Free Lunch theorem and the good efficiency of the distinctive preserve recombination (DPX) operator, we design a new recombination operator for solving a BTLP by new evolutionary and memetic algorithms namely, genetic local search algorithms (GLS). We also define the potential... 

Membrane reactors are an advanced technology with vast application capacities for equilibrium limited endothermic reactions. The main propose of this study is to offer an optimized packed-bed membrane steam methane reforming (SMR) tubular reactor for sustainable CH4 conversion by implementing triple-objective optimization model based on optimum H2/CO ratio for low temperature Fischer-Tropsch (F-T) process. In this study a one dimensional pseudo-homogeneous model based on mass, energy, and momentum conservation laws is used to simulate the behavior of a packed-bed membrane reactor for production of syngas by SMR. In the optimization section, the proposed work explores optimal values of... 

This paper develops an economic production quantity model for a multi-product multi-objective inventory control problem with fuzzy-stochastic demand and backorders. In this model, the annual demand is represented by trapezoidal fuzzy random numbers. The centroid defuzzification and the expected value methods are applied to defuzzify and make decisions in a random environment. In the case where the warehouse space is limited, the Lagrangian relaxation procedure is first employed to determine the optimal order and the maximum backorder quantities of the products such that the total inventory cost is minimized. The optimal solution obtained by the proposed approach is compared with that... 

This article presents a method for optimizing and controlling an oil production system using a natural gas lift concept. Ever increasing development of Smart Well technology and various applications of down-hole monitoring and controlling instruments along with new methods of data acquisition/transmission make it possible for the natural gas lift system to be controlled and optimized more effectively and faster than before. With this technology it is possible to monitor the down-hole conditions of gas and oil zones and to control the inflow valves in gas and oil zones. In this work, a proportional integral differential feedback controller has been used to smartly control the entrance of gas... 

In this paper, two different hybrid intelligent systems are applied to develop practical soft identifiers for modeling the tool-tissue force as well as the resulted maximum local stress in laparoscopic surgery. To conduct the system identification process, a 2D model of an in vivo porcine liver was built for different probing tasks. Based on the simulation, three different geometric features, i.e. maximum deformation angle, maximum deformation depth and width of displacement constraint of the reconstructed shape of the deformed body are extracted. Thereafter, two different fuzzy inference paradigms are proposed for the identification task. The first identifier is an adaptive co-evolutionary... 

The implementation of Monte Carlo simulations (MCSs) for the propagation of uncertainty in real-world seawater intrusion (SWI) numerical models often becomes computationally prohibitive due to the large number of deterministic solves needed to achieve an acceptable level of accuracy. Previous studies have mostly relied on parallelization and grid computing to decrease the computational time of MCSs. However, another approach which has received less attention in the literature is to decrease the number of deterministic simulations by using more efficient sampling strategies. Sampling efficiency is a measure of the optimality of a sampling strategy. A more efficient sampling strategy requires... 

Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is... 

Bayesian optimization algorithm (BOA) utilizes a Bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed Bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of Bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each Bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is... 

A transient is defined as an event when a plant proceeds from a normal state to an abnormal state. In nuclear power plants (NPPs), recognizing the types of transients during early stages, for taking appropriate actions, is critical. Furthermore, classification of a novel transient as "don't know", if it is not included within NPPs collected knowledge, is necessary. To fulfill these requirements, transient identification techniques as a method to recognize and to classify abnormal conditions are extensively used. The studies revealed that model-based methods are not suitable candidates for transient identification in NPPs. Hitherto, data-driven methods, especially artificial neural networks... 

This paper presents a fuzzy based modified artificial bee colony (MABC) algorithm to solve discrete optimal power flow (OPF) problem that has both discrete and continuous variables considering valve point effects. The OPF problem is formulated as a multi-objective mixed-integer nonlinear problem, where optimal settings of the OPF control variables for simultaneous minimization of total fuel cost of thermal units, total emission, total real power losses, and voltage deviation are obtained. The proposed approach is applied to the OPF problem on IEEE 30-bus and IEEE 118-bus test systems. The performance and operation of the proposed approach is compared with the conventional methods. The... 

Recently, a hybrid Lattice Boltzmann Level Set Method (LBLSM) for two-phase incompressible fluids with large density differences, in cases of negligible or a priori known pressure gradients, has been proposed. In the present work, the mentioned LBLSM method is extended to take into account pressure gradient effects. The lattice Boltzmann method is used for calculating velocities, the interface is captured by the level set function, and the surface tension is replaced by an equivalent body force. The method can be applied to simulate two-phase fluid flows with density ratios up to 1000 and viscosity ratios up to 100. In order to validate the method, the evolution and merging of rising bubbles... 

Computer aided history matching techniques are increasingly playing a role in reservoir characterization. This article describes the implementation of a differential evolution optimization algorithm to carry out reservoir characterization by conditioning the reservoir simulation model to production data (history matching). We enhanced the differential evolution algorithm and developed the modified differential evolution optimization method with random localization. The proposed technique is simple-structured, robust, and computationally efficient. We also investigated the convergence characteristics of the algorithm in some synthetic oil reservoirs. In addition, the proposed method is... 

New heuristic filters are proposed for state estimation of nonlinear dynamic systems based on particle swarm optimization (PSO) and differential evolution (DE). The methodology converts state estimation problem into dynamic optimization to find the best estimate recursively. In the proposed strategy the particle number is adaptively set based on the weighted variance of the particles. To have a filter with minimal parameter settings, PSO with exponential distribution (PSO-E) is selected in conjunction with jDE to self-adapt the other control parameters. The performance of the proposed adaptive evolutionary algorithms i.e. adaptive PSO-E, adaptive DE and adaptive jDE is studied through a... 

This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee... 

Remotely sensed data obtained through satellite imaging is a useful tool for modeling environmental phenomena such as drought. In this manuscript, we apply optimized echo state networks to model and predict drought severity based on satellite images. To this end, a model is constructed in which the satellite-based vegetation index is fed as an input and drought severity index is obtained as output. We use a Kronecker-based approach to reduce the number of parameters of echo state networks to be optimized (i.e., the internal weights of reservoir). A number of evolutionary algorithms are used to optimize the parameters, of Differential Evolution results in the best performance as compared to... 

This article proposes a novel algorithm to improve the lifetime of a wireless sensor network. This algorithm employs swarm intelligence algorithms in conjunction with compressive sensing theory to build up the routing trees and to decrease the communication rate. The main contribution of this article is to extend swarm intelligence algorithms to build a routing tree in such a way that it can be utilized to maximize efficiency, thereby rectifying the delay problem of compressive sensing theory and improving the network lifetime. In addition, our approach offers accurate data recovery from small amounts of compressed data. Simulation results show that our approach can effectively extend the...