Top PDF Modeling and Optimisation of Distribution Networks Using Hybrid Genetic Algorithms: A Comparative Study

Modeling and Optimisation of Distribution Networks Using Hybrid Genetic Algorithms: A Comparative Study

Modeling and Optimisation of Distribution Networks Using Hybrid Genetic Algorithms: A Comparative Study

he distribution network directly and critically affects the structure, complexity, costs and overall efficiency associated with operating a Supply Chain (SC), as well as the service level, the two critical factors in any SC [1]. This directly influences the aptitude of participants in a SC (whether is under control of a single or a conglomerate of companies) to enter or stay competitive in a market. An aspect of the utmost importance is the escalating complexity of distribution networks. As SC become increasingly large and complex, a general trend today, due mainly to globalization [2], designing the distribution network becomes vital [3], [4]. Due to sheer size, capacity of classical tools to solve and optimise LA problems in real distribution networks was exceeded. For this reason,
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Hybrid Feature Based War Scene Classification using ANN and SVM: A Comparative Study

Hybrid Feature Based War Scene Classification using ANN and SVM: A Comparative Study

The first neurological network model was introduced by McCulloch and Pitts [19]. The Hebbian rule[20] represents neural learning procedures, which implies that the connection between two neurons is strengthened when both neurons are active at the same time. In [21], Werbos developed a learning procedure called backpropagation of error. Later on, the backpropagation of error learning procedure was separately developed and published by parallel distributed processing group [22], in which weights and biases are adjusted by error- derivative (delta) vectors backpropagated through the network. Backpropagation is commonly applied to feedforward multilayer networks. Sometimes this rule is called the generalized delta rule. Numerous ANN models are constructed; the differences in them might be the functions, the accepted values, the topology, the learning algorithms, etc.
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A HYBRID MULTI-OBJECTIVE BAYESIAN ESTIMATION OF DISTRIBUTION ALGORITHM THESIS

A HYBRID MULTI-OBJECTIVE BAYESIAN ESTIMATION OF DISTRIBUTION ALGORITHM THESIS

Nowadays, a number of metaheuristics have been developed for dealing with multiobjective optimization problems. Estimation of distribution algorithms (EDAs) are a special class of metaheuristics that explore the decision variable space to construct probabilistic models from promising solutions. The probabilistic model used in EDA captures statistics of decision variables and their interdependencies with the optimization problem. Moreover, the aggregation of local search methods can notably improve the results of multi-objective evolutionary algorithms. Therefore, these hybrid approaches have been jointly applied to multi-objective problems. In this work, a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), which is based on a Bayesian network, is proposed to multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and configuration parameters of an embedded local search (LS). We tested different versions of HMOBEDA using instances of the multi-objective knapsack problem for two to five and eight objectives. HMOBEDA is also compared with five cutting edge evolutionary algorithms (including a modified version of NSGA-III, for combinatorial optimization) applied to the same knapsack instances, as well to a set of MNK-landscape instances for two, three, five and eight objectives. An analysis of the resulting Bayesian network structures and parameters has also been carried to evaluate the approximated Pareto front from a probabilistic point of view, and also to evaluate how the interactions among variables, objectives and local search parameters are captured by the Bayesian networks. Results show that HMOBEDA outperforms the other approaches. It not only provides the best values for hypervolume, capacity and inverted generational distance indicators in most of the experiments, but it also presents a high diversity solution set close to the estimated Pareto front.
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J. Braz. Soc. Mech. Sci. & Eng.  vol.27 número2

J. Braz. Soc. Mech. Sci. & Eng. vol.27 número2

A quasi-steady aerodynamic approach has been adopted to yield the expressions of lift (L), drag (D) and aerodynamic moment (M) in the hovering flight condition (Marques, 1993). The induced velocity, which yields a free airflow velocity parallel to the y-axis, has been neglected. The small displacement consideration results in the assumption that the blade cross-section remains parallel to the yz plane. Mass and elastic axes are not coincident, but the aerodynamic centre is taken at the same point of the elastic axis and cross-section intersection. The NACA 0015 airfoil has been assumed, which leads to coincident blade cross-section aerodynamic and pressure centres. A blade element dx has been taken and the corresponding load element has been computed. Considering that the blade elastic displacements in the free air flow and an operational region for the blade angle of attack, the aerodynamic loading results:
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Effect Of Perception Use Of Information Technology Safety And Perception Of Interest Trust Online Trading System Using Internet Banking Study Empris In Bri Bank Branch Office Tangerang 2014

Effect Of Perception Use Of Information Technology Safety And Perception Of Interest Trust Online Trading System Using Internet Banking Study Empris In Bri Bank Branch Office Tangerang 2014

internet banking is a facility that is very helpful to have increased from year to year. But on the other hand, it seems obvious that the facilities of Internet banking has not been fully utilized by the community in Indonesia. On the other hand the people who perform manual transactions still feel that with the transactions they feel more comfortable and confident that the transaction run perfectly, coupled with still a lack of understanding regarding the use of information technology, especially in online transactions using internet banking system. On the other side of the bank, which acts as the service provider mentions that the low risk that happening, but if further investigation services also have a high risk so slowly - the land can discourage users against internet banking services so expect banks also need to review and determine any factors that could affect services so that services can be developed. There are several factors that can affect the condition of the weakness of internet banking services, among others; First, the quality of internet banking services have not been evenly distributed. This makes the customer is often doomed to failure transaction that resulted in disappointment. Second, reliability and security. Some modes crimes include forging website (modus crimes where the perpetrator makes viewing and website domain address exactly with the original bank website so that customers be fooled and actors can easily obtain a username and password. The website is also equipped with a key- logger (a virus the hidden and the duty to record every key typed user input keyboard to get the username and password of customers) so that this virus will record any data. Third, as well as SMS banking and mobile banking, internet banking also does
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Lat. Am. j. solids struct.  vol.8 número4

Lat. Am. j. solids struct. vol.8 número4

Structural optimization using computational tools has be- come a major research field in recent years. Methods com- monly used in structural analysis and optimization may de- mand considerable computational cost, depending on the problem complexity. Therefore, many techniques have been evaluated in order to diminish such impact. Among these various techniques, Artificial Neural Networks (ANN) may be considered as one of the main alternatives, when com- bined with classic analysis and optimization methods, to reduce the computational effort without affecting the final solution quality. Use of laminated composite structures has been continuously growing in the last decades due to the ex- cellent mechanical properties and low weight characterizing these materials. Taken into account the increasing scien- tific effort in the different topics of this area, the aim of the present work is the formulation and implementation of a computational code to optimize manufactured complex lam- inated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimiza- tion and ANN to approximate the finite element solutions. The modules for linear and geometrically non-linear static fi- nite element analysis and for optimize laminated composite plates and shells, using GA, were previously implemented. Here, the finite element module is extended to analyze dy- namic responses to solve optimization problems based in fre- quencies and modal criteria, and a perceptron ANN module is added to approximate finite element analyses. Several ex- amples are presented to show the effectiveness of ANN to approximate solutions obtained using the FEM and to re- duce significatively the computational cost.
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ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (<i>CAMELLIA SINENSIS</i> (L.)) USING HYPERSPECTRAL TECHNIQUES

ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (<i>CAMELLIA SINENSIS</i> (L.)) USING HYPERSPECTRAL TECHNIQUES

2.6 A hybrid approach (nonlinear regression approach) For one tea variety growing in the greenhouse, the neural network approach were applied to build the spectral-chemical relationship using nonlinear regression way. A one hidden layer feed-forward, error-back propagation artificial neural network were adopted in this research, for this algorithm has been frequently and successfully used in previous studies (Skidmore et al. 1997). To find the optimal number of nodes in the hidden layer, we investigated the training and test accuracies using different number of neurons (1-20) in the network (the maximum number was designed no more than 20 to keep the model parsimony and save the calculation time). Levenberg- Marquardt optimization method was used to train the networks in which the parameters of networks were adjusted adaptively (Lera and Pinzolas, 2002; More, 1978) and an earlier stop technique was applied in this study to avoid overtraining (Lin and Chen, 2004).
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EURASIAN MINERAL WATER: MATHEMATICAL MODELING, CLASSIFICATION AND ASSESSMENT OF THEIR IMPACT ON THE BIOCHEMICAL COMPOSITION OF HUMAN BLOOD

EURASIAN MINERAL WATER: MATHEMATICAL MODELING, CLASSIFICATION AND ASSESSMENT OF THEIR IMPACT ON THE BIOCHEMICAL COMPOSITION OF HUMAN BLOOD

Abstract. In the article we give the results of comparative analysis of the composition of the Eurasian hydromineral resources and we implement the assessment of their impact on the physiological condition of an organism of the person according to biochemical studies of venous blood. Processing of initial data on the composition and properties of mineral waters chloride-hydrocarbonate, sulphate-hydrocarbonate and chloride- sulphate types and venous blood are made using the method of mathematical modeling, developed by the authors of this article. It is shown that in the balneological impact hydromineral resources on the body hemoglobin and oxygen in the blood increases, glucose decreases, and acid-base pH shifts to high alkalinity.
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Genetic, ecological and morphological divergence between populations of the endangered Mexican Sheartail hummingbird (Doricha eliza).

Genetic, ecological and morphological divergence between populations of the endangered Mexican Sheartail hummingbird (Doricha eliza).

Nearctic-Neotropical migrants [37,81]. Despite the observed genetic differentiation between the two populations of D. eliza, the question of how their isolation occurred remains unanswered. It is likely that the Veracruz population represents a relatively recent colonization event, though it is difficulty to directly observe immigration events in nature [82]. Colonization has been more important than large-scale vicariance in determining the phyloge- netic structure of hummingbird faunas, particularly the insular Mellisugini species assemblage of the West Indies [83], owing to their high dispersal ability, their capacity to adapt to novel environments [82–83], and the fact that migratory behavior can evolve rapidly in response to selection [37]. The Mellisugini are highly opportunistic generalists that, seasonally and altitudinally, cover large distances to track floral resources [5,84–85]. These migratory habits confer a natural vagility and may have predisposed them to fly long distances and tolerate a wide range of ecological regimes [84]. Although migratory behavior might have increased the colonization success of Mellisugini in the West Indies and remote geographic areas with a seasonal climate, vagrancy does not appear to predict the colonization of oceanic islands or remote areas [83], and it is not known whether migratory Mellisugini species are more prone to vagrancy than sedentary hummingbird species such as the Mexican Sheartail. An alternative explanation is that ancestral colonizers arrived naturally from Yucatan to Veracruz, a direction potentially assisted by the prevailing east-to-west trade winds and hurricanes. Our estimates of historical gene flow indicating a general trend of unidirectional gene flow between populations correspond to a Yucatan-to-Veracruz direction of historical migration.
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Function Approximation of Seawater Density Using Genetic Algorithm

Function Approximation of Seawater Density Using Genetic Algorithm

Kuo, Hu, and Chen [15] used a hybrid model incorporating Radial Basis Neural Network (RBNN) genetic algorithms, Particle Swarm Optimization (PSO), and Self Organizing Map (SOM). RBNN was used however as the core approximator where the genetic algorithm was employed to enhance the results. Kuo, Hu, and Chen [15] noted that blending genetic algorithm with RBNN in one model is complicating the model and extending the computation time though it performs better. So, they proposed to inject Self-Organizing map in the procedure to reduce the effect of the complexity of the model on the computation time and combine the evolving feature of the genetic algorithm with the memory preserving nature of PSO to ensure the diversity in the population. The implementation was carried on different functions based on standalone models and their proposed hybrid model. Their results brings the least percentages of errors.
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Affine image registration using genetic algorithms and evolutionary strategies

Affine image registration using genetic algorithms and evolutionary strategies

This thesis investigates the application of evolutionary algorithms to align two or more 2-D images by means of image registration. The proposed search strategy is a transformation parameters-based approach involving the affine transform. A noisy ob- jective function is proposed and tested using two well-known evolutionary algorithms (EAs), the genetic algorithm (GA) as well as the evolutionary strategies (ES) that are suitable for this particular ill-posed problem. In contrast with GA, which was originally designed to work on binary representation, ES was originally developed to work in contin- uous search spaces. Surprisingly, results of the proposed real coded genetic algorithm are far superior when compared to results obtained from evolutionary strategies’ framework for the problem at hand. The real coded GA uses Simulated Binary Crossover (SBX), a parent-centric recombination operator that has shown to deliver a good performance in many optimization problems in the continuous domain. In addition, a new technique for matching points, between a warped and static images by using a randomized ordering when visiting the points during the matching procedure, is proposed. This new tech- nique makes the evaluation of the objective function somewhat noisy, but GAs and other population-based search algorithms have been shown to cope well with noisy fitness eval- uations. The results obtained from GA formulation are competitive to those obtained by the state-of-the-art classical methods in image registration, confirming the usefulness of the proposed noisy objective function and the suitability of SBX as a recombination operator for this type of problem.
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Why do cryptic species tend not to co-occur? A case study on two cryptic pairs of butterflies.

Why do cryptic species tend not to co-occur? A case study on two cryptic pairs of butterflies.

The phylogenetic trees based on COI sequences (S2 Fig.) resulted in clearly differentiated clades for each species in accordance with previous studies [21,22]. Among the regressions used to correct the genetic distances according to geographic distances, the relationship of orig- inal p-distances against log-transformed geographic distances showed the best fit. As expected for the reduced geographic scale involved in the Delaunay triangulation, the asymptotic regres- sion failed in finding any significant solution. We thus computed and interpolated the residuals between p-distance and log-geographic distance. The resulting genetic divergence landscapes and the assignment of individuals to a species based on their position in the phylogenetic tree indicated, for both cryptic pairs, parapatry on mainland with a contact zone in the Iberian Pen- insula, and not a single case of coexistence on islands (Fig. 2). For Polyommatus, the strongest genetic divergence with respect to geographic distance corresponded to abrupt changes of dis- tributions across the narrow Messina and Bonifacio straits, along the Tyrrhenian Sea between Italy and Sardinia, between the Balearics and Iberia and between northern and southern Iberia. In southern Iberia the picture became more complex due to the existence of isolated popula- tions of P. Icarus in particular mountaintops, such as Sierra Nevada and Sierra de La Sagra. For Aricia, the highest divergence emerged across the Bonifacio strait, along the Tyrrhenian Sea, between North Africa and Sicily, between the Balearics and France, in Catalonia and along the Pyrenees. As a result of intraspecific divergence, minor differentiation was also found between Corsica and the Tuscan Archipelago and between Sicily and the Italian Peninsula. In summary, for both species pairs the most pronounced genetic differences were located over sea areas, con- firming that sea straits have strong power in the formation and maintenance of non-
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Neural Network Based 3D Surface Reconstruction

Neural Network Based 3D Surface Reconstruction

Shape recovery is a classical computer vision problem. The objective of shape recovery is to obtain a three-dimensional (3-D) scene description from one or more two-dimensional (2- D) images. Shape recovery from shading (SFS) is a computer vision approach, which reconstructs 3-D shape of an object from its shading variation in 2-D images. When a point light source illuminates an object, they appear with different brightness, since the normal vectors corresponding to different parts of the object’s surface are different. The spatial variation of brightness, referred to as shading, is used to estimate the orientation of surface and then calculate the depth map of the object
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DESIGNING DAILY PATROL ROUTES FOR POLICING BASED ON ANT COLONY ALGORITHM

DESIGNING DAILY PATROL ROUTES FOR POLICING BASED ON ANT COLONY ALGORITHM

The benchmark test is also conducted using the same environment. Previous research has shown that optimal patrolling can be obtained if all agents follow the same TSP or Hamilton cycle, equally distributed in time and space (Smith and Rus 2010; Pasqualetti et al. 2012). However, this is based on topological representation of the environment in which patrolling targets are treated as points. In this work an adjusted algorithm is adopted to fit the problem that patrolling targets are segments with physical length. The problem to solve, known as the Rural Postman Problem (Christofides et al. 1981), is to find a shortest circuit that traverses a subset of required segments (hotspots) at least once of a connected undirected graph. One well-known algorithm for this problem, the Christofides Algorithm (Christofides et al. 1981), is used in this work as the benchmark strategy. Note that this is a heuristic solution and has been proved that in the case where the underlying network satisfies the triangular inequality property, the performance of this algorithm has a bound of 3/2, which means the performance is bound accordingly: (Christofides Solution)/(Optimal Solution) 3/2(Pearn and Wu 1995). In the following discussion, the patrolling strategy based on Christofides algorithm is referred to as the Christofides Cycle Patrolling Strategy (CCPS).
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A Convergence Indicator for Multi-Objective Optimisation Algorithms

A Convergence Indicator for Multi-Objective Optimisation Algorithms

This indicator has been using by the community since 2003. Basically, the hypervolume of a set of solutions measures the size of the portion of objective space that is dominated by those solutions as a group. In general, hypervolume is favored because it captures in a single scalar both the closeness of the solutions to the optimal set and, to some extent, the spread of the solutions across objective space. There are many works on this indicator such as in [13] which the author studied how expensive to calculate this indicator was. Few years later, it was proposed a faster alternative by using Monte Carlo simulation ( See [14]) that it was addressed for many objectives problem by Monte Carlo simulation. In the order to get a right definition, you can look at [14, 13].
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Voice Recognition with Neural Networks, Type-2 Fuzzy Logic and Genetic Algorithms

Voice Recognition with Neural Networks, Type-2 Fuzzy Logic and Genetic Algorithms

Using cepstral analysis as described in the previous section, an utterance may be represented as a sequence of feature vectors. Utterances spoken by the same person but at different times result in similar yet a different sequence of feature vectors. The purpose of voice modeling is to build a model that captures these variations in the extracted set of features. There are two types of models that have been used extensively in speaker verification and speech recognition systems: stochastic models and template models. The stochastic model treats the speech production process as a parametric random process and assumes that the parameters of the underlying stochastic process can be estimated in a precise, well-defined manner. The template model attempts to model the speech production process in a non-parametric manner by retaining a number of sequences of feature vectors derived from multiple utterances of the same word by the same person. Template models dominated early work in speaker verification and speech recognition because the template model is intuitively more reasonable. However, recent work in stochastic models has demonstrated that these models are more flexible and hence allow for better modeling of the speech production process. A very popular stochastic model for modeling the speech production process is the Hidden Markov Model (HMM). HMMs are extensions to the conventional Markov models, wherein the observations are a probabilistic function of the state, i.e., the model is a doubly embedded stochastic process where the underlying stochastic process is not directly observable (it is hidden). The HMM can only be viewed through another set of stochastic processes that produce the sequence of observations.
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Improving the Solution of Traveling Salesman Problem Using Genetic, Memetic Algorithm and Edge assembly Crossover

Improving the Solution of Traveling Salesman Problem Using Genetic, Memetic Algorithm and Edge assembly Crossover

Those traditional algorithms such as Cupidity Algorithm, Dynamic Programming Algorithm, are all facing the same obstacle, which is when the problem scale N reaches to a certain degree, the so- called “Combination Explosion” will occur. A lot of algorithms have been proposed to solve TSP. Some of them (based on dynamic programming or branch and bound methods) provide the global optimum solution. Other algorithms are heuristic ones, which are much faster, but they do not guarantee the optimal solutions. The TSP was also approached by various modern heuristic methods, like simulated annealing, evolutionary algorithms and tabu search, even neural networks. In this paper, we proposed a new algorithm based on Inver-over operator, for traveling salesman problems. In the new algorithm we will use new strategies including selection operator, replace operator and some new control strategy, which have been proved to be very efficient to accelerate the converge speed.[5]
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Development and Modeling of Hydro-formed circular sheet Using Neural Networks

Development and Modeling of Hydro-formed circular sheet Using Neural Networks

During the sheet hydro forming process, the work p iece or sheet meta l is placed over the die. The blank holder and cylinder combination is placed over the die. The bolts are properly tightened with the die and blank holder. Abov e the cylinder the punch is bolted with the piston of the hydraulic cylinder. The hydraulic piston is wounded by spring in the hydraulic cylinder and it moves by stroke of hydraulic pressure. The punch and piston are connected by bolts. The total hydraulic cylinder is welded on rectangular holding device. Above the hydraulic cylinder the top base plate is welded with a holding device. The container (oil tank) is connected to rectangular holding device by bolts. The container (oil tank) is built by two piston such as high pressure piston and low pressure piston. With this, a relief valve, four ball and threads are arranged inside the container (oil tank).The container (oil tank) and hydraulic cylinder are connected by a T-pipe through which oil flo ws during the sheet hydro forming process.
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Genetic terrain programming

Genetic terrain programming

To the best of our knowledge, Teong Ong et al. [16] were the first authors to propose an evolutionary approach to generate terrains. They proposed an evolutionary design optimisation technique to generate terrains by applying genetic algorithms to transform height maps in order to conform them to the required features. Their approach breaks down the terrain generation pro- cess into two stages: the terrain silhouette generation phase, and the terrain height map generation phase. The input to the first phase is a rough, 2D map laying out the geography of the desired terrain that can be randomly generated or specified by the designer. This map is processed by the first phase to remove any unnaturally straight edges and then fed to the second phase, along with a database of pre-selected height map samples representa- tive of the different terrain types. The second phase searches for an optimal arrangement of elevation data from the database that approximates the map generated in the first phase. Since the height map generation algorithm is inherently random, the terrains generated from two separate runs of the al- gorithm will not be the same, even if they use the same map. While this has the benefit of allowing an infinite number of variations to be created, it could also inhibit the designer’s creation process, as each successive run would produce an entirely new terrain, even if the designer had made only a small tweak to the map between runs. To control this, the seed for the random number generator can be kept the same across separate runs of the algorithm, allowing the same terrain to be regenerated as many times as desired.
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DESIGN AND OPTIMIZATION OF VALVELESS MICROPUMPS BY USING GENETIC ALGORITHMS APPROACH

DESIGN AND OPTIMIZATION OF VALVELESS MICROPUMPS BY USING GENETIC ALGORITHMS APPROACH

Recently, micropumps have been designed with various types of actuator used as a device for control on mechanical movement according to the micropump’s application. Valveless micropumps are commonly driven by a piezoelectric element attached on the flexible diaphragm. By applying voltage on the piezoelectric material induces deformation on the diaphragm which creates displacement and generates pressure head inside the pump chamber. The ability of the micropump to direct the working fluid inside the chamber is based upon the flow resistance in the diffuser elements. Typical characteristics of piezoelectric actuators include large actuation force, fast response time and simple structure. Figure 1 shows the cross< section of piezoelectric micropump with piezo disc on the diaphragm.
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