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Detection & Classification of power quality (PQ) disturbances are the most important problems in distribution systems. In this paper, a new approach for the detection and classification of single and combined PQ disturbances is proposed which utilizes fuzzy logic and particle swarm optimization (PSO) algorithms. In this approach, first suitable features of the waveform of PQ disturbances are extracted. Extraction of these specifications is done based on the Fourier and Wavelet transforms. Then, the proposed Fuzzy systems make decision about the type of each of the PQ disturbances by employing these specifications. The PSO algorithm is used for accurate determination of each parameter of the membership functions of the systems. To test the proposed approach, the waveform of PQ disturbances was assumed to be in a sampled form from single and combined categories. Impulse, interruption, swell, sag, notch, transient, harmonic, and flicker phenomena are considered as single disturbances for voltage signal. More over, harmonic with swell, swell with harmonic, swell with transient, harmonic with sag, sag with harmonic and sag with transient are considered as combined disturbances for the voltage signal. Simulation results denote capability of the proposed approach for identification of single and combined disturbances with about 99% accuracy.

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The echoes obtained from ultrasonic testing of materials contain valuable information about the geometry and grain structure of the test specimen. These echoes can be modeled by Gaussian pulses in a model-based estimation process. For precise modeling of an echo, the parameters of the Gaussian pulse should be estimated as accurately as possible. There are a number of algorithms that can be used for this purpose. In this study, three different algorithms are used: Gauss-Newton (GN), particle swarm optimization (PSO), and genetic algorithm (GA). The pros and cons of each of these three algorithms are reviewed and by combining them, the benefits of each algorithm are used while its shortcomings are avoided. For signals containing multiple echoes, the minimum description length (MDL) principle is used to estimate the numbers of required Gaussian echoes followed by space alternating generalized expectation maximization (SAGE) technique to translate it to separate echoes and to estimate the parameters of each echo. The performance of the proposed algorithms for simulated and experimental signals with overlapping and non-overlapping echoes is evaluated and shows to be quite effective.

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The consumption expenditure is a key element of macroeconomic analyses, which accounts for considerable share of aggregate demand in Iran. Any effort for forecasting the future consumption trend is of special importance for policy-makers. In this paper, we specify a consumption model relying on theoretical basics in order to obtain desirable forecasts. On the basis of Duesenberry and Friedman consumption theories, we use genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to simulate Iranians consumption during 1973-2009. Then we select the superior model in terms of prediction power criteria and forecast consumption until 2025. According to the results, the PSO algorithm is efficient and accurate in forecasting consumption; and consumption behavior of Iranians is consistent with Duesenberry theory. In addition, the simulations by exponential consumption model indicate increasing average propensity to consume until 2025.  

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Supplier selection and determination of the lot sizing is an important component of production and logistics management for many companies. Therefore, after the select of preferred suppliers at the first should obtaine the optimize order of each of the suppliers that is the purposes and constraints of determiners. One of the most effective techniques, which can provide optimal solutions with different targets, is multi-objective programming model.Purpose of this study is to design an efficient multi-objective model of optimal to determine the lot sizing to each supplier.This work is done with designation of multi- objective model, to achieve minimizing the cost of the chain, such as the cost of purchasing, storage, transportation, etc., and also maximize the quality of materials that purchased from suppliers. Finally the model is solved by using the meta-heuristic method, multi-objective Non-dominated Sorting Genetic Algorithm II (NAGA-II), and also in order to validate the model using meta-heuristic particle swarm optimization algorithm (PSO) has been solved and results compared with the first method.

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There are many factors causing damages to a structure, including earthquakes, winds, environmental effects, etc. In order to repair a damaged structure, first its damage locations should be identified. Therefore, the damage identification of structures is considered as an important issue in civil engineering as well as mechanical engineering. Many methodologies have been devised for damage identification of structures, which are generally categorized to destructive and non-destructive cases. As a non-destructive damage identification approach, solving inverse problems for identifying the properties of a damaged structure is one of the popular methods which utilizes an optimization algorithm to minimize an error function in terms of measured strains or displacements. Since an iterative procedure with significant number of structural analyses should be carried out for the optimization process, an efficient numerical method should be employed to reduce the total computational cost. In this paper, the identification of hole in two-dimensional continuum structures is investigated with finite cell method and particle swarm optimization algorithm. The finite cell method is an efficient numerical method for solving the governing equations of continuum structures having geometrical complexity and/or discontinuities, which uses the concept of virtual domain method. The use of this concept makes the mesh generation easier such that the simple structured meshes can be utilized even for the curved boundaries of a structure, and hence mesh refinement is not necessary for the problems like damage detection. The finite cell method uses adaptive numerical integration for the cells including non-uniform material distribution. Accordingly, quadtree integration is utilized for the structural analysis using the finite cell method. Consequently, the computational time is significantly reduced. On the other hand, particle swarm optimization is a well-known meta-heuristic algorithm, and hence it does not require the gradient information of the problem. This population-based algorithm has been inspired by the social behaviour of animals such as fish schooling and birds flocking. The basis of this algorithm relies on the social influence and learning which enable individuals to preserve cognitive consistency. Thus, the exchange of ideas and interactions between individuals can lead them to solve optimization problems like damage detection. This study proposes the finite cell method and particle swarm optimization algorithm for damage detection of plate structures with single hole or multiple holes. As a non-gradient-based method, particle swarm optimization explores the search space to find the coordinates of the existing damage by minimizing an error function. This error function is evaluated by the strains or displacements calculated by the structural analysis utilizing the finite cell method. In order to evaluate the proposed methodology, numerical examples are provided to demonstrate the capability of finite cell method and particle swarm optimization algorithm in damage detection of two-dimensional structures. The first example considers the damage detection of a plate with a single hole, and it also considers the effects of mesh density. The second example employs a plate structure with three holes. The results demonstrate that the proposed methodology, with suitable computational efforts, can successfully be applied to damage detection of these structures.