• The Journal of the Korea Contents Association
• /
• v.8 no.1
• /
• pp.290-299
• /
• 2008

We propose the query optimization technique using query classification in hybrid spatial DBMS. In our approach, user queries should to be classified into three types: memory query, disk query, and hybrid query. Specialty, In the hybrid query processing, the query predicate is divided by comparison between materialized view creating conditions and user query conditions. Then, the deductions of the classified queries' cost formula are used for the query optimization. The optimization is mainly done by the selection algorithm of the smallest cost data access path. Our approach improves the performance of hybrid spatial DBMS than traditional disk-based DBMS by $20%{\sim}50%$.

• Journal of the Optical Society of Korea
• /
• v.14 no.4
• /
• pp.342-350
• /
• 2010

An optimal wideband gain flattened hybrid erbium-doped fiber amplifier/fiber Raman amplifier (EDFA/FRA) has been introduced. A new and effective optimization method called particle swarm optimization (PSO) is employed to find the optimized parameters of the EDFA/FRA. Numerous parameters which are the parameters of the erbium-doped fiber amplifier (EDFA) and the fiber Raman amplifier (FRA) define the gain spectrum of a hybrid EDFA/FRA. Here, we optimize the length, $Er^{3+}$ concentration, and pump power and wavelength of the EDFA and also pump powers and wavelengths of the FRA to obtain the flattest operating gain spectrum. Hybrid EDFA/FRA with 6-pumped- and 10-pumped-FRAs have been studied. Gain spectrum variations are 1.392 and 1.043 dB for the 6-pumped- and 10-pumped-FRAs, respectively, in the 108.5 km hybrid EDFA/FRAs, with 1 mW of input signal powers. Dense wavelength division multiplexing (DWDM) system with 60 signal channels in the wavelength range of 1529.2-1627.1 nm, i.e. the wide bandwidth of 98 nm, is studied. In this work, we have added FRA's pump wavelengths to the optimization parameters to obtain better results in comparison with the results presented in our previous works.

• Structural Engineering and Mechanics
• /
• v.46 no.3
• /
• pp.403-416
• /
• 2013

This paper describes new optimization strategy that offers significant improvements in performance over existing methods for geometry design of frame structures. In this study, an imperialist competitive algorithm (ICA) and ant colony optimization (ACO) are combined to reach to an efficient algorithm, called Imperialist Competitive Ant Colony Optimization (ICACO). The ICACO applies the ICA for global optimization and the ACO for local search. The results of optimal geometry for three benchmark examples of frame structures, demonstrate the effectiveness and robustness of the new method presented in this work. The results indicate that the new technique has a powerful search strategies due to the modifications made in search module of ICACO. Higher rate of convergence is the superiority of the presented algorithm in comparison with the conventional mathematical methods and non hybrid heuristic methods such as ICA and particle swarm optimization (PSO).

• Journal of Korean Association for Spatial Structures
• /
• v.19 no.3
• /
• pp.51-59
• /
• 2019

A hybrid mid-story seismic isolation system with a smart damper has been proposed to mitigate seismic responses of tall buildings. Based on previous research, a hybrid mid-story seismic isolation system can provide effective control performance for reduction of seismic responses of tall buildings. Structural design of the hybrid mid-story seismic isolation system is generally performed after completion of structural design of a building structure. This design concept is called as an iterative design which is a general design process for structures and control devices. In the iterative design process, optimal design solution for the structure and control system is changed at each design stage. To solve this problem, the integrated optimal design method for the hybrid mid-story seismic isolation system and building structure was proposed in this study. An existing building with mid-story isolation system, i.e. Shiodome Sumitomo Building, was selected as an example structure for more realistic study. The hybrid mid-story isolation system in this study was composed of MR (magnetorheological) dampers. The stiffnessess and damping coefficients of the example building, maximum capacity of MR damper, and stiffness of isolation bearing were simultaneously optimized. Multi-objective genetic optimization method was employed for the simultaneous optimization of the example structure and the mid-story seismic isolation system. The optimization results show that the simultaneous optimization method can provide better control performance than the passive mid-story isolation system with reduction of structural materials.

• Journal of KSNVE
• /
• v.10 no.3
• /
• pp.437-443
• /
• 2000

The optimal design problem for increasing dynamic stiffness using hybrid model which composed of original detailed BIW(body in white) and impinged beam elements is investigated. Using the characteristics of the beam elements and design sensitivity analysis this approach utilizes an optimization technique to determine the optimal section properties of beam elements. The constraint is to increase the first natural frequency by five percent compared with original one. The results show that the first torsion and bending natural frequencies are increased by five percent using hybrid model and optimization. These results indicate that this optimization method can be employed to enhance the dynamic stiffness of vehicle body structure in design concept stage.

• Journal of the Korean Society for Precision Engineering
• /
• v.26 no.3
• /
• pp.129-136
• /
• 2009

The estimation of muscle force is important to understand the roles of the muscles. The static optimization method can be used to figure out the individual muscle forces. However, muscle forces during the movement including muscle co-contraction cannot be considered by the static optimization. In this study, a hybrid static optimization method was introduced to find the well-matched muscle forces with EMG signals under muscle co-contraction conditions. To validate the developed algorithm, the 3D motion analysis and its corresponding inverse dynamics using the musculoskeletal modeling software (SIMM) were performed on heel-rise movements. Results showed that the developed algorithm could estimate the acceptable muscle forces during heel-rise movement. These results imply that a hybrid numerical approach is very useful to obtain the reasonable muscle forces under muscle co-contraction conditions.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.11 no.2
• /
• pp.809-818
• /
• 2019

The current trend in shipyard industry is to reduce the weight of ships to support the reduction of CO₂ emissions. In this study, the stiffened plate was optimized that is used for building most of the ship-structure. Further, this study proposed the hybrid Genetic Algorithm (GA) technique, which combines a genetic algorithm and subsequent optimization methods. The design variables included the number and type of stiffeners, stiffener spacing, and plate thickness. The number and type of stiffeners are discrete design variables that were optimized using the genetic algorithm. The stiffener spacing and plate thickness are continuous design variables that were determined by subsequent optimization. The plate deformation was classified into global and local displacement, resulting in accurate estimations of the maximum displacement. The optimization result showed that the proposed hybrid GA is effective for obtaining optimal solutions, for all the design variables.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.3
• /
• pp.1027-1037
• /
• 2017

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA) and hybrid Bacterial Foraging Particle Swarm Optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

• Proceedings of the KIEE Conference
• /
• 2007.04c
• /
• pp.74-76
• /
• 2007

A hybrid optimization method is proposed for cogging torque reducing in BLDC motor. The proposed hybrid optimization method comprises a response surface method (RSM) and a gradient search method (GSM). The RSM is effective and global method in optimization problem but having large approximation error. The GSM is accurate and fast search method for optimal solution but having local behavior. To reduce approximation error and computation time a hybrid method (RSM+GSM) is proposed method. To illustrate the effectiveness of the proposed method, a comparison between conventional RSM and the proposed hybrid method is made. A simulation results verify that the hybrid method can achieve favorable design performance.

• Journal of Computing Science and Engineering
• /
• v.8 no.4
• /
• pp.199-206
• /
• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.