• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.3
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• pp.397-403
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• 2013

The demand for eco-friendly and higher fuel economy vehicles has helped develop eco-friendly and fuel-efficient vehicles such as hybrid vehicles. In a hybrid vehicle, the change in the battery charge after driving should be added to the fuel consumption as the equivalent fuel usage based on its own characteristics. Thus, the fuel efficiency of a hybrid vehicle cannot be improved simply by increasing the battery capacity. In this study, I attempt to improve the total fuel economy of a hybrid vehicle, including the equivalent fuel consumption, by modeling a fuel cell hybrid vehicle using Matlab Simulink, analyzing the usage zone of the fuel cell with the existing control strategy, and optimizing the power distribution of the battery and fuel cell in the main usage zone of the fuel cell.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.792-797
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• 2011

This paper discusses the application of a hybrid discretiziation method for the discretization procedure that needs to be included in discrete particle swarm optimization (DPSO) for the problem of allocating PV (photovoltaic) systems onto distribution power systems. For this purpose, this paper proposes a rule-based expert system considering the objective function value and its optimizing speed as the input parameters and applied it to the PV allocation problem including discrete decision variables. For multi-level discretization, this paper adopts a hybrid method combined with a simple rounding and sigmoid funtion based 3-step and 5-step quantization methods, and the application of the rule based expert system proposing the adequate discretization method at each PSO iteration so that the DPSO with the hybrid discretization can provide better performance than the previous DPSO.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1391-1407
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• 1990

In this paper, optimization procedures are presented considering the static and dynamic constraints for laminated composite plate and hybrid laminated composite plate subject to concentrated load on center of the plates. Design variables for this problem are ply angle or ply thickness. Deflection, natural frequency and specific damping capacity are considered as constraints. Using a recursive linear programming method, the nonlinear optimization problems are solved. By introducing the design scaling factor, the number of iterations is reduced significantly. Composite plates could be designed optimally combined with FEM analysis under various conditions. In the optimization procedure, verification for both analysis and design of the laminated composite plates are compared with the results of the others. Various design results are presented for the laminated composite plates and hybrid laminated composite plates.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.48-55
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• 2010

Reliability has been considered as a one of the major design measures in various industrial and military systems. The main objective is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for the problem that determines the optimal component reliability to maximize the system reliability under cost constraint in this study. Reliability optimization problem has been known as a NP-hard problem and normally formulated as a mixed binary integer programming model. Component structure, reliability, and cost were computed by using HPGA and compared with the results of existing meta-heuristic such as Ant Colony Optimization(ACO), Simulated Annealing(SA), Tabu Search(TS) and Reoptimization Procedure. The global optimal solutions of each problem are obtained by using CPLEX 11.1. The results of suggested algorithm give the same or better solutions than existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improving solution through swap and 2-opt processes.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• Industrial Engineering and Management Systems
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• v.6 no.1
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• pp.64-71
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• 2007

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.3
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• pp.250-258
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• 2018

Free Standing Hybrid Riser (FSHR) is comprised of vertical steel risers and Flexible Jumpers (FJ). They are jointly connected to a submerged Buoyancy Can (BC). There are several factors that have influence on the behavior of FSHR such as the span distance between an offshore platform and a foundation, BC up-lift force, BC submerged location and FJ length. An optimization method through a parametric study is presented. Firstly, descriptions for the overall arrangement and characteristics of FSHR are introduced. Secondly, a flowchart for optimization of FSHR is suggested. Following that, it is described how to select reasonable ranges for a parametric study and determine each of optimal configuration options. Lastly, numerical analysis based on this procedure is performed through a case study. In conclusion, the relation among those parameters is analyzed and non-dimensional parametric ranges on optimal arrangements are suggested. Additionally, strength analysis is performed with variation in the configuration.

• ETRI Journal
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• v.42 no.3
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• pp.333-340
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• 2020

We consider a hybrid combiner design for downlink massive multiple-input multiple-output systems when there is residual inter-user interference and each user is equipped with a limited number of radio frequency (RF) chains (less than the number of receive antennas). We propose a hybrid combiner that minimizes the mean-squared error (MSE) between the information symbols and the ones estimated with a constant amplitude constraint on the RF combiner. In the proposed scheme, an iterative alternating optimization method is utilized. At each iteration, one of the analog RF and digital baseband combining matrices is updated to minimize the MSE by fixing the other matrix without considering the constant amplitude constraint. Then, the other matrix is updated by changing the roles of the two matrices. Each element in the RF combining matrix is obtained from the phase component of the solution matrix of the optimization problem for the RF combining matrix. Simulation results show that the proposed scheme performs better than conventional matrix-decomposition schemes.