• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.115-125
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• 2015

This paper presents a Cuckoo search algorithm to secure adversaries misdirecting multi-hop routing in Mobile ad hoc networks (MANETs) using a robust Trust Predicated Routing Framework with an optimized cluster head selection. The clustering technique designed in this framework leads to efficient routing in MANETs. The heavy work load in the node causes an energy drop in cluster head, which leads to re-clustering of the group, and another cluster head is selected to avoid packet loss during data transmission. The problem in the re-clustering process is that the overall efficiency of the routing process is reduced and the processing time is increased. A Cuckoo search based optimization algorithm is proposed to solve the problem of re-clustering by selecting the secondary cluster head within the initially formed cluster group and eliminating the reclustering process. The proposed framework enables a node to select a reliable and secure route for MANET and the performance can be evaluated by comparing the simulated results with the AODV routing protocol, which shows that the performance of the proposed routing protocol are improved significantly.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.407-415
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• 2011

In this study, we propose the design of optimized fuzzy controller for the rotary inverted pendulum system by using differential evolution algorithm. The structure of the differential evolution algorithm has a simple structure and its convergence to optimal values is superb in comparison to other optimization algorithms. Also the differential evolution algorithm is easier to use because it have simpler mathematical operators and have much less computational time when compared with other optimization algorithms. The rotary inverted pendulum system is nonlinear and has a unstable motion. The objective is to control the position of the rotating arm and to make the pendulum to maintain the unstable equilibrium point at vertical position. The output performance of the proposed fuzzy controller is considered from the viewpoint of performance criteria such as overshoot, steady-state error, and settling time through simulation and practical experiment. From the result of both simulation and practical experiment, we evaluate and analyze the performance of the proposed optimal fuzzy controller from the comparison between PGAs and differential evolution algorithms. Also we show the superiority of the output performance as well as the characteristic of differential evolution algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8A
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• pp.821-828
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• 2008

In this paper, we present an optimized design of RS(23,17) decoder for UWB, which uses the pipeline structured-modified Euclidean(PS-ME) algorithm. Firstly, the modified processing element(PE) block is presented in order to get rid of degree comparison circuits, registers and MUX at the final PE stage. Also, a degree computationless decoding algorithm is proposed, so that the hardware complexity of the decoder can be reduced and high-speed decoder can be implemented. Additionally, we optimize Chien search algorithm, Forney algorithm, and FIFO size for UWB specification. Using Verilog HDL, the proposed decoder is implemented and synthesized with Samsung 65nm library. From synthesis results, it can operate at clock frequency of 250MHz, and gate count is 17,628.

• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.17-23
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• 2024

Various methods have been developed to predict the flight path of an air-launched weapon to intercept a fast-moving target in the air. However, it is also getting more challenging to predict the optimal firing zone and provide it to a pilot in real-time during engagements for advanced weapons having new complicated guidance and thrust control. In this study, a method is proposed to develop an optimized weapon engagement zone by the SCG (Scaled Conjugate Gradient) algorithm to achieve both accurate and fast estimates and provide an optimized launch display to a pilot during combat engagement. SCG algorithm is fully automated, includes no critical user-dependent parameters, and avoids an exhaustive search used repeatedly to determine the appropriate stage and size of machine learning. Compared with real data, this study showed that the development of a machine learning-based weapon aiming algorithm can provide proper output for optimum weapon launch zones that can be used for operational fighters. This study also established a process to develop one of the critical aircraft-weapon integration software, which can be commonly used for aircraft integration of air-launched weapons.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1237-1253
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• 2018

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• Wind and Structures
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• v.20 no.3
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• pp.363-388
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• 2015

For controlling the vibration of specific building structure with large span, a practical method for the design of MTMD was developed according to the characteristics of structures subjected to wind loads. Based on the model of analyzing wind-induced response of large-span structure with MTMD, the optimization method of multiple tuned mass dampers for large-span roof structures subjected to wind loads was established, in which the applicable requirements for strength and fatigue life of TMD spring were considered. According to the method, the controlled modes and placements of TMDs in MTMD were determined through the quantitative analysis on modal contribution to the wind-induced dynamic response of structure. To explore the characteristics of MTMD, the parametric analysis on the effects of mass ratio, damping ratio, central tuning frequency ratio and frequency range of MTMD, was performed in the study. Then the parameters of MTMD were optimized through genetic algorithm and the optimized MTMD showed good dynamic characteristics. The robustness of the optimized MTMD was also investigated.

• Journal of Power Electronics
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• v.15 no.2
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• pp.479-486
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• 2015

The application of soft-switching techniques is increasing in the DC/DC converter area. It is important to design soft-switching parameters to ensure the converter operates properly and efficiently. An optimized design method is presented in this paper. The objective function is the total power loss of a converter, while the variables are soft-switching parameters and the constraints are the electrical requirements for soft-switching. Firstly, a response surface methodology (RSM) model with a high precision is built, and the rough optimized parameters can be obtained with the help of a genetic algorithm (GA) in the solution space determined by the constraints. Secondly, a re-optimization is conducted with a SPICE model and a GA, and accurate optimized parameters can be obtained. Simulation and experiment results show that the proposed method performs well in terms of a wide adaptability, efficiency, and global optimization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2886-2896
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• 1996

The Type of Service based multicast routing algorithm is necessary to support efficiently herogeneous applications in ATM network. In this paper I propose the Constrained Multicast Tree with Virtual Destination(DMTVD) heuristic algorithm as least cost multicast routing algorithm. The service is categorized into two types, as delay sensitive and non in CMTVD algorithm. For the delay sensitive service type, the cost optimized route is the Minimum Cost Stenier Tree connecting all the destination node group, virtual destination node group and source node with least costs, subject to the delay along the path being less than the maximum allowable end to end delay. The other side for the non-delay sensitive service, the cost optimized route is the MCST connecting all the multicast groups with least costs, subject to the traffic load is balanced in the network. The CMTVD algorithm is based on the Constrained Multicasting Tree algorithm but regards the nodes branching multiple destination nodes as virtural destination node. The experimental results show that the total route costs is enhanced 10%-15% than the CTM algorithm.