Sharif Digital Repository

The present study investigated a counter-flow cooling tower performance by integrating the Artificial Neural Networks and Intelligent Optimisation Algorithms (ANN-IOAs). For this purpose, two scenarios were evaluated. In the first scenario, inlet air wet-bulb temperature (T aw), inlet air dry bulb temperature (T ad), water to the air mass flow rate ratio (mw /ma), and rotor speed (υ) were the input parameters for the ANNs, while the output temperature (T wo) was the ANNs output. In the second scenario, the same input parameters applied for the first scenario were used as input variables and the tower efficiency (ε) was considered as an output parameter. The well-known IOAs methods, namely,... 

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)... 

Particle Swarm Optimization (PSO) is a well-known swarm intelligence (SI) algorithm employed for solving various optimization problems. This algorithm suffers from premature convergence to local optima. Accordingly, a number of PSO variants have been proposed in the literature. These algorithms exploited different schemes to improve performance. In this paper, we propose a new variant of PSO with an enhanced Learning strategy and Crossover operator (PSOLC). This algorithm applies three strategies, comprising altering the exemplar particles, updating the PSO parameters, and integrating PSO with Genetic Algorithm (GA). In the proposed learning strategy, each particle is guided by the best... 

In this research, the economic lot scheduling problem (ELSP), as an NP-hard problem in terms of a bi-objective approach considering deteriorating items and shortage, is studied. The goal is to simultaneously minimise ‘setup and inventory holding costs, comprising deterioration’, and ‘total amount of units facing shortage throughout every period. Two policies besides a heuristic method are employed simultaneously, named extended basic period and Power-of-Two (PoT), to make sure of having feasible replenishment cycles. For handling the considered problem, three multi-objective techniques are employed: non-dominated sorting genetic algorithm II (NSGAII), non-dominated ranking genetic algorithm... 

The objective of this research is geometrical and kinematical optimization of full-toroidal continuously variable transmission (CVT) in order to achieve high power transmission efficiency and low mass. At first, a dynamic analysis is performed for the system. A computer model is developed to simulate elastohydrodynamic (EHL) contact between disks and roller and consequently, calculate CVT efficiency. The validity of EHL model is investigated by comparing output of this model and experimental data. Geometrical parameters are obtained by means of Particle Swarm Optimization algorithm, while the optimization objective is to maximize CVT efficiency and minimize its mass. The algorithm is run for... 

This paper presents a particle swarm optimization (PSO) based fuzzy stochastic long term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level, and reduction in voltage deviation are simultaneously considered as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other, then they are introduced to a PSO... 

This research deals with a flexible flowshop scheduling problem with the arrival and delivery of jobs in groups and processing them individually. Each group of jobs has a specific release time. Due to the special characteristics of each job, only a specific group of machines in each stage are eligible to process that job. All jobs in a group should be delivered at the same time after processing. The objectives of the problem are the minimization of the sum of the completion time of groups on one hand and the minimization of sum of the differences between the completion time of jobs and the delivery time of the group containing that job (waiting period) on the other hand. The problem can be... 

One of the recently developed approaches for control of chaos is the minimum entropy (ME) control technique. In this method an entropy function based on the Shannon definition, is defined for a chaotic system. The control action is designed such that the entropy as a cost function is minimized which results in more regular pattern of motion for the system trajectories. In this paper an online optimization technique using particle swarm optimization (PSO) method is developed to calculate the control action based on ME strategy. The method is examined on some standard chaotic maps with error feedback and delayed feedback forms. Considering the fact that the optimization is online, simulation... 

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of... 

Aluminum metal matrix composites (MMCs) reinforced with nanoceramics are ideal materials for the manufacture of lightweight automotive and other commercial parts. Adaptive neuro-fuzzy inference system combined with particle swarm optimization method is implemented in this research study in order to optimize the parameters in processing of aluminum MMCs. In order to solve the problems associated with poor wettability, agglomeration and gravity segregation of nanoparticles in the melt, a mixture of alumina and aluminum particles was used as the reinforcement instead of raw nanoalumina. Microstructural characterization shows dendritic microstructure for the sand cast and non-dendritic... 

Refrigerators have considerable share of residential consumption. They can be, however, flexible loads because their operating time and consumption patterns can be changed to some extent. Accordingly, they can be selected as a target for the study of Demand Side Management plans. In this paper, two experimental models for a refrigerator are derived. In obtaining the first model, following assumptions are made: the ambient temperature of refrigerator is assumed to be constant and the refrigerator door is remained closed. However, in the second model the variation of ambient temperature and door-opening effects are considered according to some general patterns. Further, two strategies are... 

In multi-tower heliostat fields, although heliostats are capable of aiming at different receivers during the day, due to different orientations, neighboring heliostats might affect shading and blocking efficiency of each other reciprocally. In the proposed method of this paper, considering the mentioned effects and based on a group decision-making approach, each heliostat chooses the best receiver thus ensuring the highest possible instantaneous efficiency of the field. As a case study, this method is applied for the optimal design of a multi-tower field. Then, the field performance is simulated in a case where heliostats make decisions individually without considering the interactions.... 

In tunneling projects and open-pit mines, drilling and blasting is a common method for fragmenting the rock masses. Although fragmentation is the main aim of blasting, the adverse effects such as air-overpressure (AOp) and ground vibration are unavoidable. Among these unwanted effects, AOp is considered as one of the most important effects which can cause damage to nearby structures. Therefore, precise estimation of AOp is required for minimizing the environmental problems. This article proposes three new models for predicting blast-induced AOp at Shur river dam area, Iran, optimized by particle swarm optimization (PSO). For this aim, 80 blasting events were investigated and the requirement... 

Analysis of gamma-ray spectra is an important step for identification and quantification of radionuclides in a sample. In this paper a new gamma-ray spectra analysis algorithm based on Particle Swarm Optimization (PSO) is developed to identify different isotopes of a mixed gamma-ray source and determine their fractional abundances. PSO is an iterative algorithm that imitates the social behaviors observed in nature to solve complex optimization problems. The PSO method is used for complex fitting to the response of a 3 [Formula presented] 3 inch NaI (Tl) scintillation detector and the fitting process is controlled by a test for significance of abundance and computation of Theil coefficient.... 

Cloud Computing enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. The cloud infrastructures can be used as an appropriate environment for executing of workflow applications. To execute workflow applications in this environment, it is necessary to develop the workflow scheduling algorithms that consider different QoS parameters such as execution time and cost. Therefore, in this paper we focus on two criteria: total completion time (makespan) and execution cost of workflow, and propose two heuristic algorithms: MTDC (Minimum Time and Decreased Cost) which aims to create a schedule that minimizes the makespan and decreases execution cost, and CTDC... 

Asphaltene precipitation in petroleum industries is known as major problems. To solve problems there are approaches for inhibition of asphaltene precipitation, Asphaltene inhibitors are known effective and economical approach for inhibition and prevention of asphaltene precipitation. In the present study Adaptive neuro-fuzzy inference system (ANFIS) was coupled with Particle swarm optimization (PSO) to create a novel approach to predict effect of inhibitors on asphaltene precipitation as function of crude oil properties and concentration and structure of asphaltene inhibitors.in order to training and testing the algorithm, a total number of 75 experimental data was gathered from the... 

In tunneling projects and open-pit mines, drilling and blasting is a common method for fragmenting the rock masses. Although fragmentation is the main aim of blasting, the adverse effects such as air-overpressure (AOp) and ground vibration are unavoidable. Among these unwanted effects, AOp is considered as one of the most important effects which can cause damage to nearby structures. Therefore, precise estimation of AOp is required for minimizing the environmental problems. This article proposes three new models for predicting blast-induced AOp at Shur river dam area, Iran, optimized by particle swarm optimization (PSO). For this aim, 80 blasting events were investigated and the requirement... 

One of the important parameters in economic study of energy sources and bioenergy is higher heating value (HHV). In this investigation, adaptive neuro fuzzy inference system (ANFIS) was applied as a novel method to predict HHV of biomass in terms of fixed carbon (FC), ash content (ASH), and volatile matters (VMs). Due to the fact that experimental investigations are time- and cost-consuming, this investigation was selected purely computational and a total number of 350 experimental data were extracted from literature for different steps of modeling. The proposed algorithm was evaluated by statistical indexes such as coefficient of determination (R2), root mean squared error (RMSE), and... 

The two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor (Keff) in order to extract the maximum energy, and keeping the local power peaking factor (Pq) lower than a predetermined value to maintain fuel integrity. In this research, a new strategy based on Particle Swarm Optimization (PSO) algorithm has been developed to optimize the fuel core loading pattern in a typical VVER. The PSO algorithm presents a simple social model by inspiration from bird collective behavior in finding food. A modified version of PSO algorithm for discrete variables has been developed and implemented successfully for the multi-objective optimization... 

Anchored wall system is one of the common methods used for deep excavation stabilization adjacent to sensitive structures in urban areas. A key aspect of the stability analysis of deep excavations is the amount of deformations occurring on the facing wall and the adjacent structures. In this research, a large number of parametric studies considering all aspects of soil-structure interaction is carried out for different excavation geometries to find the optimal design, and the outcome is shown in the form of design tables and charts. Also, by a GA-PSO algorithm and using the large database obtained from the numerical simulations, a simple equation is developed that can predict the deflections...