Determination of the critical failure surface is performed in stability evaluation process for road cut slope, embankments, dam, excavations, retaining walls and many more. Finding the critical failure surface in a ro...
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Determination of the critical failure surface is performed in stability evaluation process for road cut slope, embankments, dam, excavations, retaining walls and many more. Finding the critical failure surface in a rock or soil slope is very cumbersome and becomes a difficult constrained global optimization problem. Due to existence of discontinuous function and strong multiple local minima points, researchers are facing difficulties to employ trial-and-error methods in a large search space. Thus, classical optimization techniques fail to converge to a valid solution. In this study a stochastic method called biogeography-based optimization algorithm was adopted for analyzing the factor of safety. Based on the finding from the implementation and quantitative evaluation, it was found that the proposed method for locating critical failure surface in homogeneous soil slope acquires more efficient results over other implemented methods such as grid search and genetic algorithm. The validation and effectiveness of the proposed method are investigated by solving two benchmark case studies from the literature, while the simulation design for slip surfaces is carried out using 'Rocscience slide' software tool for comparing the results.
Due to the increasing population and the pollution of existing water resources because of various factors, the need for water in the world is increasing. This requires building additional water distribution systems al...
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Due to the increasing population and the pollution of existing water resources because of various factors, the need for water in the world is increasing. This requires building additional water distribution systems along with rehabilitation of existing systems. The cost of transmission and distribution constitutes more than half of the total cost of water supply systems. For this reason, by the optimum design of pipeline systems, the cost of water supply can be reduced. In this study, the optimization of pipe diameters in three networks, Two- Loop, Hanoi and GoYang, was carried out by a combination of two metaheuristic algorithms, namely Biogeography based optimization (bbo) and invasive weed optimization (IWO) under the same constraints. In addition, the Combined Gravity Network was successfully optimized with the current method for the first time in the literature.
Due to the wide application of evolutionary science in different engineering problems, the main aim of this paper is to present two novel optimizations of multi-layer perceptron (MLP) neural network, namely dragonfly ...
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Due to the wide application of evolutionary science in different engineering problems, the main aim of this paper is to present two novel optimizations of multi-layer perceptron (MLP) neural network, namely dragonfly algorithm (DA) and biogeography-based optimization (bbo) for landslide susceptibility assessment at a study area, West of Iran. Utilizing 14 landslide conditioning factors, namely elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), and topographic wetness index (TWI) and rainfall as the input variables, and 208 historical landslides as target variable, the required spatial database is created. Then, the MLP is synthesized with the mentioned algorithms to develop the proposed DA-MLP and bbo-MLP ensembles. Three accuracy criteria of mean square error, mean absolute error, and area under the receiving operating characteristic curve are used to evaluate the performance of the models and also to develop a score-based ranking system. As the first outcome, the application of the DA and bbo metaheuristic algorithms enhances the accuracy of the MLP. Moreover, referring to the calculated total ranking scores of 6, 14, and 16, it was revealed that the bbo performs more efficiently than DA in optimizing the MLP.
In recent years, nonlinear load and inverter-based distributed generation (like photovoltaic and fuel cells) have been increasing rapidly. This type of DGs can amplify and propagate the harmonic level of network. Shun...
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In recent years, nonlinear load and inverter-based distributed generation (like photovoltaic and fuel cells) have been increasing rapidly. This type of DGs can amplify and propagate the harmonic level of network. Shunt capacitor banks and DG units allocation without considering harmonic limits will increase harmonic pollution. In this paper the biogeography-based optimization (bbo) algorithm is applied in simultaneous optimal sitting and sizing of inverter-based DGs and capacitor banks regarding multi-level and nonlinear loads. The objective of problem are reduction of active and reactive power loss, reduction of purchased energy from transmission line and improvement of voltage profile considering equal and unequal constrains. Also, effect of total harmonic distortion (THD) constraint according to IEEE 519 standard has been investigated in the objective function which is one of the main advantages of the proposed method. The proposed method is tested on IEEE 33 bus and IEEE 69 bus radial distribution systems (RDS). Comparing bbo method results with particle swarm optimization (PSO) and genetic algorithm (GA) results will indicate the high capability of the proposed method in simultaneously optimal sitting and sizing of DGs and capacitors. (c) 2016 Elsevier Ltd. All rights reserved.
This study presents a novel optimisation algorithm biogeography-based optimisation (bbo) for thinning large multiple concentric circular ring arrays. The objective is to achieve an array of uniformly excited isotropic...
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This study presents a novel optimisation algorithm biogeography-based optimisation (bbo) for thinning large multiple concentric circular ring arrays. The objective is to achieve an array of uniformly excited isotropic antennas that will generate a narrow beam with minimum relative sidelobe level (SLL). bbo is a new comprehensive force based on the science of biogeography. Biogeography is the schoolwork of geographical allotment of biological organisms. bbo utilises migration operator to share information between the problem solutions. The problem solutions are known as habitats and sharing of features is called migration. In this study, the authors propose pattern synthesis method to reduce the SLLs with narrow beamwidth (BW) by making the ring array thinned using the bbo algorithm. The thinning percentage of the array is kept equal to or more than 50% and the BW is kept equal to or less than that of a fully populated, uniformly excited and 0.5 lambda(w), spaced concentric circular ring array of same number of elements and rings. The results obtained are compared with previous published results of modified particle swarm optimisation and differential evolution with global and local neighbourhoods.
This study proposes a new integrated technique for efficient distributed generator (DG) installation and scaling in distribution systems. The Biogeography-Based Optimisation (bbo) algorithm and an Artificial Neural Ne...
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This study proposes a new integrated technique for efficient distributed generator (DG) installation and scaling in distribution systems. The Biogeography-Based Optimisation (bbo) algorithm and an Artificial Neural Network(ANN) are combined in the suggested integrated approach. The bbo algorithm determines the location of the DG. bbo is a subset of evolutionary computation that is used to address a range of combinatorial optimization problems, mostly in the transportation, location, and scheduling domains. The system is optimised using the load bus's voltage stability indices, power loss and active power. The artificial neural network (ANN) is a form of artificial intelligence (AI) methodology for estimating DG capacity. The network is trained according to the system's target using the back-propagation training technique. In the MATLAB programming environment, the proposed integrated technique is implemented to identify the optimal location of DG and ideal Size of the DG to obtain stable voltage with reduced real power losses. The proposed methodology was evaluated using the IEEE 30 bus system's major distributed bus (load bus).
Renewable energy systems (RESs) are affordable, clean and sustainable. However, their output power is intermittent. Therefore, RESs are usually combined with an energy storage system or conventional sources to make th...
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Renewable energy systems (RESs) are affordable, clean and sustainable. However, their output power is intermittent. Therefore, RESs are usually combined with an energy storage system or conventional sources to make the overall operation uninterruptable. Optimal sizing of hybrid energy system components is imperative to be financially and technically feasible. In this study, a multi-objective optimisation based on a hybrid optimisation procedure, which combines the exploitation ability of the biogeography-based optimisation (bbo) with the exploration ability of the particle swarm optimisation (PSO), is used to handle the system design. This algorithm is known as greedy particle swarm and bbo algorithm (GPSbbo). Weighted sum method is added to the GPSbbo to handle the multi-objective nature of the design problem. A case study for a hybrid wind-PV energy system design in the standalone and grid-connected configurations is presented to illustrate the proposed method. Coverage of two sets, hypervolume and diversity performance indices are used to compare results of the proposed method to non-dominated sorting genetic algorithm and the multi-objective PSO. These indices show an improved performance of the suggested method in finding the optimal system design.
Lost circulation occurs frequently during drilling, increasing drilling costs in minor cases and leading to the scrapping of wells in major cases, and plugging leakage has been the focus of related research. An effici...
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Lost circulation occurs frequently during drilling, increasing drilling costs in minor cases and leading to the scrapping of wells in major cases, and plugging leakage has been the focus of related research. An efficient leakage plugging material formulation is an essential tool to cope with this problem. In this paper, a technical solution is proposed to predict a plugging material formulation based on experimental data combined with neural network technology. First, experiments were conducted using rigid mineral particles (classified into four levels) and plugging agent composite plant fibers (classified into four levels) for drilling to obtain sufficient plugging material formulation data and perform the necessary data preprocessing. Then, the basic back propagation neural network prediction model was established, which showed a qualified prediction ability with a prediction error rate of 16.89%, but it was still far from the expected effect. On this basis, the base model was optimized using a genetic algorithm and a biogeography-based optimization algorithm;the prediction error rates were 9.05% and 5.91%, respectively, and the performance of the prediction model was significantly improved. In addition, when the prediction results were unsatisfactory, the prediction results could be improved by 19.8-26.9% using network integration as an auxiliary means. Finally, three important challenges in predicting plugging material formulations using experimental data are summarized. Overall, this study shows that neural networks are a practical solution for predicting drilling plugging material formulations and have great research value and potential in dealing with plugging problems and that rapid access to effective drilling plugging measures can help people deal with lost circulation events quickly and reduce losses.
In this paper, a novel algorithm is designed to detect the community structure of network systems in the smart city based on the biogeography-based optimization (bbo) algorithm and the Newman, Moore, and Watts (NMW) s...
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In this paper, a novel algorithm is designed to detect the community structure of network systems in the smart city based on the biogeography-based optimization (bbo) algorithm and the Newman, Moore, and Watts (NMW) small-world network. We have incorporated the NMW small-world network to the bbo algorithm to enhance the ability of migration of the habitat by using the connection mechanism of the NMW small-world network. With the help of small-world network information sharing, the convergence speed of the bbo algorithm has significantly improved. The first step of the algorithm design is to generate an NMW small-world network containing nodes equal to the number of habitats with good connectivity, which facilitates better information exchange between the nodes. In the second step, the habitat in the bbo algorithm is dynamically assigned to the small world network, and then, the bbo algorithm migrates and mutates according to the connection relationship of the NMW small-world network. Finally, the new designed NMW-bbo algorithm is evaluated for community detection via four real networks and computer-generated networks, and one of them is exhibited the characteristics of a large network. The numeric simulations are also employed to demonstrate that the new algorithm exhibits better accuracy and robustness.
This paper deals with the problem optimising the quality of service (QoS) in a multicast routing with multiple constraints (multi constrained least-cost multicast routing problem). To solve this problem, we propose tw...
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This paper deals with the problem optimising the quality of service (QoS) in a multicast routing with multiple constraints (multi constrained least-cost multicast routing problem). To solve this problem, we propose two approaches. The first one named biogeography-based optimisation (bbo) is inspired by the biogeography mathematics;the second, named bat algorithm (BA) is inspired from bat behaviour. We propose to use a tree structure to represent the solution. We start optimising the graph representing the network. We propose to generate the initial population with greedy heuristic using depth first search (DFS). Then, we optimise the initial population solutions in order to minimise the execution time. We have adapted bbo and BA operators to deal with the discrete nature of the problem and the structures representing the problem solution. In experiments, we have used Waxman topology model to generate the graph. The results were encouraging in comparison with other existing algorithms.
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