887 research outputs found

    Improved dynamical particle swarm optimization method for structural dynamics

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    A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version

    Application of artificial intelligence to evaluate the fresh properties of self-consolidating concrete

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    This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase

    Structural optimization of self-supported dome roof frames under gust wind loads

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    PhD ThesisDome roofs are large structures often subject to variable wind, snow and other loading conditions, in addition to their own weight. A wide variety of structural designs are used in practice, and finding the optimal arrangement of trusses or girders, along with suitable section properties, is a common subject for structural optimization studies. This thesis focuses on self-supported dome roofs for fuel storage tanks, and a variety of optimization techniques are adapted, developed and compared. Various load conditions have been compared using detailed fluid and stress analysis in ANSYS. From results for full and empty storage tanks, with wind and/or snow external loads, the worst cases are for wind loading alone, i.e., snow loading counters the lift force from the wind. Consequently, the case of an empty fuel storage tank subject to wind loading is used as the basis for the structural optimization. To speed up the optimization, a simplified frame analysis was developed in Matlab and integrated with the optimization code. In addition, the wind loads were modelled in ANSYS for a range of dome radii and imported into the Matlab, and a number of different dome designs were used as case studies: these were ribbed, Schwedler, Lamella and geodesic. The principal method used to optimize the frame is Morphing Evolutionary Structural Optimization (MESO), in which an initial overdesigned frame is iteratively analysed and reduced in overall weight by reducing the sections of key frame members. The frame is progressively weakened, but without compromising the structural integrity, until it is no longer possible to reduce the weight. However, there are additional parameters that MESO is not suited to, such as dome radius and those affecting the overall structure of the dome frame (numbers and placements of rings, etc.), and a variety of metaheuristic optimization techniques have been studied: Artificial Bee Colony (ABC), Bees Algorithm (BA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Simulated Annealing (SA). These can be used instead of MESO, or in a hybrid form where MESO optimizes the frame member sections. Although the focus in this thesis is on minimizing the total structural weight, the importance of other characteristics of the design, especially structural stiffness, is considered and also integrated with the MESO process. The hybrid methods MESO-ABC and MESO-DE performed best overall.Higher Committee for Education Development (HCED), IRA

    Application of whale algorithm optimizer for unified power flow controller optimization with consideration of renewable energy sources uncertainty

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    Purpose. In this paper an allocation methodology of Flexible Alternating Current Transmission Systems (FACTS) controllers, more specifically, the Unified Power Flow Controller (UPFC) is proposed. As the penetration of Renewable Energy Sources (RESs) into the conventional electric grid increases, its effect on this location must be investigated. Research studies have shown that the uncertainty of RESs in power generation influences the reactive power of a power system network and consequently its overall transmission losses. The novelty of the proposed work consists in the improvement of voltage profile and the minimization of active power loss by considering renewable energy sources intermittency in the network via optimal location of UPFC device. The allocation strategy associates the steady-state analysis of the electrical network, with the location and adjustment of controller parameters using the Whale Optimization Algorithm (WOA) technique. Methodology. In order to determine the location of UPFC, approaches are proposed based on identification of a line which is the most sensitive and effective with respect to voltage security enhancement, congestion alleviation as well as direct optimization approach. The optimum location of UPFC in the power system is discussed in this paper using line loading index, line stability index and optimization method. The objective function is solved using the WOA algorithm and its performance is evaluated by comparison with Particle Swarm Optimization (PSO) algorithm. Results. The effectiveness of the proposed allocation methodology is verified through the analysis of simulations performed on standard IEEE 30 bus test system considering different load conditions. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used to overcome the optimum location issue. Additionally, the results show that when UPFC device is strategically positioned in the electrical network and uncertainty of RES is considered, there is a significant influence on the overall transmission loss and voltage profile enhancements of the network.Мета. У статті пропонується методологія розподілу контролерів гнучких систем передачі змінного струму (FACTS), зокрема уніфікованого контролера потоку потужності (UPFC). Оскільки проникнення відновлюваних джерел енергії (ВДЕ) у звичайну електричну мережу збільшується, необхідно досліджувати їхній вплив на це. Наукові дослідження показали, що невизначеність ВДЕ у виробленні електроенергії впливає на реактивну потужність мережі енергосистеми і, отже, на її загальні втрати під час передачі. Новизна запропонованої роботи полягає в покращенні профілю напруги та мінімізації втрат активної потужності за рахунок обліку перемежування відновлюваних джерел енергії в мережі за рахунок оптимального розташування пристрою UPFC. Стратегія розподілу пов'язує стаціонарний аналіз електричної мережі з розміщенням та налаштуванням параметрів контролера з використанням методу алгоритму оптимізації кита (WOA). Методологія. Для визначення розташування UPFC пропонуються підходи, засновані на виявленні лінії, яка є найбільш чутливою та ефективною з точки зору підвищення безпеки за напругою, зменшення навантажень, а також прямий підхід до оптимізації. Оптимальне розташування UPFC в енергосистемі обговорюється в цій статті з використанням індексу завантаження лінії, індексу стійкості лінії та методу оптимізації. Цільова функція вирішується з використанням алгоритму WOA, а її продуктивність оцінюється шляхом порівняння з алгоритмом оптимізації рою частинок (PSO). Результати. Ефективність запропонованої методології розподілу перевірена за допомогою аналізу моделювання, виконаного на тестовій системі стандартної шини IEEE 30 з урахуванням різних умов навантаження. Отримані результати демонструють, що за допомогою запропонованого підходу виходять здійсненні та ефективні рішення, які можна використовувати для подолання проблеми оптимального розташування. Крім того, результати показують, що коли пристрій UPFC стратегічно розташований в електричній мережі і враховується невизначеність ВДЕ, це значно впливає на загальні втрати при передачі і поліпшення профілю напруги в мережі

    Dynamic Economic Dispatch For Power System

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    The research work in this dissertation deals with dynamic economic dispatch problem for large power systems. The work mathematically proves the dynamicity of the economic dispatch. Many physical and operational constraints were considered in the model of the dynamic economic dispatch problem. The problem is to optimize the total generation costs while satisfying the operational constraints. Through an appropriate utilization of the structural features of the model, a solution algorithm based on Particle Swarm Optimization is developed. The performance of the PSO-based developed algorithm was tested on simple case studies with a small number of generation units and limited constraints, and then on more complex case studies with a large number of variables and complicated constraints. The solution algorithm based on a constraint relaxation and period-by-period is developed and tested. The last part of the dissertation is dedicated to the comparison of solution results obtained by using PSO method and the Dantzig-Wolfe decomposition method for different cases of size and complexity. This research finds large variable size DED problems can be easily implemented, PSO method is reliable and is suitable for real-time analysis. Also, time-segmentation of the solution, or as known as a period by period solution, always results in sub-optimality, while, only by solving the optimization problem in totality can lead to an optimal solution. By modifying constraints, the method can provide alternate solutions to the dispatcher. Trade-offs between the level of convergence to the global solution and the required execution time necessitate finding a mean to enhance the social component and determine an appropriate value that leads to limiting the search space of the swarm

    Improvements on the bees algorithm for continuous optimisation problems

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    This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems
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