Abstract: The proper number and appropriate locations of service centers can save cost, raise revenue and gain more satisfaction from customers. Establishing service centers is high-cost and difficult to relocate. In long-term planning periods, several factors may affect the service. One of the most critical factors is uncertain demand of customers. The opened service centers need to be capable of serving customers and making a profit although the demand in each period is changed. In this work, the capacitated location-allocation problem with stochastic demand is considered. A mathematical model is formulated to determine suitable locations of service centers and their allocation to maximize total profit for multiple planning periods. Two heuristic methods, a local search and genetic algorithm, are used to solve this problem. For the local search, five different chances to choose each type of moves are applied. For the genetic algorithm, three different replacement strategies are considered. The results of applying each method to solve numerical examples are compared. Both methods reach to the same best found solution in most examples but the genetic algorithm provides better solutions in some cases.
Abstract: Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.
Abstract: This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.
Abstract: In the present research, various formulations of wavelet transform are applied on acceleration time history of earthquake. The mentioned transforms decompose the strong ground motion into low and high frequency parts. Since the high frequency portion of strong ground motion has a minor effect on dynamic response of structures, the structure is excited by low frequency part. Consequently, the seismic response of structure is predicted consuming one half of computational time, comparing with conventional time history analysis. Towards reducing the computational effort needed in seismic optimization of structure, seismic optimization of a shear frame structure is conducted by applying various forms of mentioned transformation through genetic algorithm.
Abstract: This work investigates an intermodal transportation system for delivering goods from a Regional Distribution Centre to supermarkets on the Isle of Wight (IOW) via the port of Southampton or Portsmouth in the UK. We consider this integrated logistics chain as a 3-echelon transportation system. In such a system, there are two types of transport methods used to deliver goods across the Solent Channel: one is accompanied transport, which is used by most supermarkets on the IOW, such as Spar, Lidl and Co-operative food; the other is unaccompanied transport, which is used by Aldi. Five transport scenarios are studied based on different transport modes and ferry routes. The aim is to determine an optimal delivery plan for supermarkets of different business scales on IOW, in order to minimise the total running cost, fuel consumptions and carbon emissions. The problem is modelled as a vehicle routing problem with time windows and solved by genetic algorithm. The computing results suggested that accompanied transport is more cost efficient for small and medium business-scale supermarket chains on IOW, while unaccompanied transport has the potential to improve the efficiency and effectiveness of large business scale supermarket chains.
Abstract: Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.
Abstract: The importance of fibre reinforced plastics continually
increases due to the excellent mechanical properties, low material
and manufacturing costs combined with significant weight reduction.
Today, components are usually designed and calculated numerically
by using finite element methods (FEM) to avoid expensive laboratory
tests. These programs are based on material models including
material specific deformation characteristics. In this research project,
material models for short glass fibre reinforced plastics are presented
to simulate the visco-elasto-plastic deformation behaviour. Prior
to modelling specimens of the material EMS Grivory HTV-5H1,
consisting of a Polyphthalamide matrix reinforced by 50wt.-% of
short glass fibres, are characterized experimentally in terms of
the highly time dependent deformation behaviour of the matrix
material. To minimize the experimental effort, the cyclic deformation
behaviour under tensile and compressive loading (R = −1) is
characterized by isothermal complex low cycle fatigue (CLCF)
tests. Combining cycles under two strain amplitudes and strain
rates within three orders of magnitude and relaxation intervals
into one experiment the visco-elastic deformation is characterized.
To identify visco-plastic deformation monotonous tensile tests
either displacement controlled or strain controlled (CERT) are
compared. All relevant modelling parameters for this complex
superposition of simultaneously varying mechanical loadings are
quantified by these experiments. Subsequently, two different material
models are compared with respect to their accuracy describing the
visco-elasto-plastic deformation behaviour. First, based on Chaboche
an extended 12 parameter model (EVP-KV2) is used to model cyclic
visco-elasto-plasticity at two time scales. The parameters of the
model including a total separation of elastic and plastic deformation
are obtained by computational optimization using an evolutionary
algorithm based on a fitness function called genetic algorithm.
Second, the 12 parameter visco-elasto-plastic material model by
Launay is used. In detail, the model contains a different type of a
flow function based on the definition of the visco-plastic deformation
as a part of the overall deformation. The accuracy of the models is
verified by corresponding experimental LCF testing.
Abstract: This work denotes an insight into dynamic synthesis of multibody systems. A set of mechanism parameters design variable are synthetized based on a desired mechanism response, such as, velocity, acceleration and bodies deformations. Moreover, knowing the work space, for a robot, and mechanism response allow defining optimal parameters mechanism handling with the desired target response. To this end, evolutionary genetic algorithm has been deployed. A demonstrative example for imperfect mechanism has been treated, mainly, a slider crank mechanism with a flexible connecting rod. The transversal deflection of the connecting rod has been chosen as response to identify the mechanism design parameters.
Abstract: This paper presents the evaluation of performance (BSFC and BTE), combustion (Pmax) and emission (CO, NOx, HC and smoke opacity) parameters of karanja biodiesel in a single cylinder, four stroke, direct injection diesel engine by considering significant engine input parameters (blending ratio, compression ratio and load torque). Multi-objective optimization of performance, combustion and emission parameters is also carried out in a karanja biodiesel engine using hybrid RSM-NSGA-II technique. The pareto optimum solutions are predicted by running the hybrid RSM-NSGA-II technique. Each pareto optimal solution is having its own importance. Confirmation tests are also conducted at randomly selected few pareto solutions to check the authenticity of the results.
Abstract: Optimization is necessary for finding appropriate solutions to a range of real-life problems. In particular, genetic (or more generally, evolutionary) algorithms have proved very useful in solving many problems for which analytical solutions are not available. In this paper, we present an optimization algorithm called Dynamic Schema with Dissimilarity and Similarity of Chromosomes (DSDSC) which is a variant of the classical genetic algorithm. This approach constructs new chromosomes from a schema and pairs of existing ones by exploring their dissimilarities and similarities. To show the effectiveness of the algorithm, it is tested and compared with the classical GA, on 15 two-dimensional optimization problems taken from literature. We have found that, in most cases, our method is better than the classical genetic algorithm.
Abstract: In this study, a black box modeling of the coupled-tank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutionary computation method, genetic algorithm. All tuned controllers are applied to the fuzzy model of the coupled-tank experimental setup and analyzed under the different reference input values. According to the results, it is seen that function tuner method demonstrates better robust control performance and guarantees the closed loop stability.
Abstract: In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.
Abstract: This paper presents the trajectory tracking control of a
spatial redundant hybrid manipulator. This manipulator consists of
two parallel manipulators which are a variable geometry truss (VGT)
module. In fact, each VGT module with 3-degress of freedom (DOF)
is a planar parallel manipulator and their operational planes of these
VGT modules are arranged to be orthogonal to each other. Also, the
manipulator contains a twist motion part attached to the top of the
second VGT module to supply the missing orientation of the endeffector.
These three modules constitute totally 7-DOF hybrid
(parallel-parallel) redundant spatial manipulator. The forward
kinematics equations of this manipulator are obtained, then,
according to these equations, the inverse kinematics is solved based
on an optimization with the joint limit avoidance. The dynamic
equations are formed by using virtual work method. In order to test
the performance of the redundant manipulator and the controllers
presented, two different desired trajectories are followed by using the
computed force control method and a switching control method. The
switching control method is combined with the computed force
control method and genetic algorithm. In the switching control
method, the genetic algorithm is only used for fine tuning in the
compensation of the trajectory tracking errors.
Abstract: The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.
Abstract: The material selection problem is concerned with the
determination of the right material for a certain product to optimize
certain performance indices in that product such as mass, energy
density, and power-to-weight ratio. This paper is concerned about
optimizing the selection of the manufacturing process along with the
material used in the product under performance indices and
availability constraints. In this paper, the material selection problem
is formulated using binary programming and solved by genetic
algorithm. The objective function of the model is to minimize the
total manufacturing cost under performance indices and material and
manufacturing process availability constraints.
Abstract: The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.
Abstract: The change of conditions for production companies in
high-wage countries is characterized by the globalization of
competition and the transition of a supplier´s to a buyer´s market. The
companies need to face the challenges of reacting flexibly to these
changes. Due to the significant and increasing degree of automation,
assembly has become the most expensive production process.
Regarding the reduction of production cost, assembly consequently
offers a considerable rationalizing potential. Therefore, an
aerodynamic feeding system has been developed at the Institute of
Production Systems and Logistics (IFA), Leibniz Universitaet
Hannover. This system has been enabled to adjust itself by using a
genetic algorithm. The longer this genetic algorithm is executed the
better is the feeding quality. In this paper, the relation between the
system´s setting time and the feeding quality is observed and a
function which enables the user to achieve the minimum of the total
feeding time is presented.
Abstract: In this paper, the unstable angle of attack of a
FOXTROT aircraft is controlled by using Genetic Algorithm based
flight controller and the result is compared with the conventional
techniques like Tyreus-Luyben (TL), Ziegler-Nichols (ZN) and
Interpolation Rule (IR) for tuning the PID controller. In addition, the
performance indices like Mean Square Error (MSE), Integral Square
Error (ISE), and Integral Absolute Time Error (IATE) etc. are
improved by using Genetic Algorithm. It was established that the
error by using GA is very less as compared to the conventional
techniques thereby improving the performance indices of the
dynamic system.
Abstract: In this paper genetic based test data compression is
targeted for improving the compression ratio and for reducing the
computation time. The genetic algorithm is based on extended pattern
run-length coding. The test set contains a large number of X value
that can be effectively exploited to improve the test data
compression. In this coding method, a reference pattern is set and its
compatibility is checked. For this process, a genetic algorithm is
proposed to reduce the computation time of encoding algorithm. This
coding technique encodes the 2n compatible pattern or the inversely
compatible pattern into a single test data segment or multiple test data
segment. The experimental result shows that the compression ratio
and computation time is reduced.
Abstract: A mixed method for model order reduction is
presented in this paper. The denominator polynomial is derived by
matching both Markov parameters and time moments, whereas
numerator polynomial derivation and error minimization is done
using Genetic Algorithm. The efficiency of the proposed method can
be investigated in terms of closeness of the response of reduced order
model with respect to that of higher order original model and a
comparison of the integral square error as well.