• Journal of Ocean Engineering and Technology
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• v.35 no.2
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• pp.131-140
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• 2021

The paper deals with comparative study of various surrogate models based approximate optimization in the structural design of the passive type deck support frame under design load conditions. The passive type deck support frame was devised to facilitate both transportation and installation of 20,000 ton class topside. Structural analysis was performed using the finite element method to evaluate the strength performance of the passive type deck support frame in its initial design stage. In the structural analysis, the strength performances were evaluated for various design load conditions. The optimum design problem based on surrogate model was formulated such that thickness sizing variables of main structure members were determined by minimizing the weight of the passive type deck support frame subject to the strength performance constraints. The surrogate models used in the approximate optimization were response surface method, Kriging model, and Chebyshev orthogonal polynomials. In the context of numerical performances, the solution results from approximate optimization were compared to actual non-approximate optimization. The response surface method among the surrogate models used in the approximate optimization showed the most appropriate optimum design results for the structure design of the passive type deck support frame.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.6
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• pp.677-682
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• 2011

In this study, a simulation model developed for a hydraulic breaker is verified through comparison of simulation results to the experimental results. For performance optimization, the design parameters are selected based on the physical parameters of the equipment for ease of modification. Also a factorial experiment and regression analysis were conducted to observe the effect of each parameter on the performance of the hydraulic breaker. As a result, a method for optimizing the performance of a hydraulic breaker is proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5013-5034
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• 2019

In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.

• The Journal of Information Technology and Database
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• v.7 no.2
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• pp.47-59
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• 2000

In this paper, we analyze the applicability of a general optimization algorithm to a database optimization problem. The index selection problem Is the problem to choose a set of indexes for a database in a way that the cost to process queries in the given workload is minimized subject to a given storage space restriction for storing indexes. The problem is well known in database research fields, and many optimization and/or heuristic algorithms have been proposed. Our work differs from previous research in that we formalize the problem in the form of non-linear Integer Programming model, and investigate the feasibility and applicability of a general purpose optimization algorithm, called OPBDP, through experiments. We implemented algorithms to generate workload data sets and problem instances for the experiment. The OPBDP algorithm, which is a non-linear 0-1 Integer Programming problem solver based on Davis-Putnam method, worked generally well for our problem formulation. The experiment result showed various performance characteristics depending on the types of decision variables, variable navigation methods and ocher algorithm parameters, and indicates the need of further study on the exploitation of the general purpose optimization techniques for the optimization problems in database area.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.650-655
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• 2012

This paper proposed PSO-Fuzzy controller design method. We could improve the learning performance of fuzzy controller by using PSO algorithm, which had recently showed its robust of performance while solving various difficult optimization problems. In other words, our aim was to forward the controller is performance by deciding fuzzy model structure that had good performance on optimization of the controller, based on PSO. During a simulation, we could see whether the mobile robot could convergence on the final goal or not, and also see the error, and through this process, we found out that this controller is more robust than the conventional controller.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.10
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• pp.659-664
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• 2000

This paper presents a new design method for improving the torque performance of a switched reluctance motor (SRM) for high speed applications. The drawback of the conventional design method based on the overall static average torque maximization is that the torque control performance is degraded at high speed. On the other hand, the proposed method optimizes the torque profile by diving it into several regions so that it is suitable for high speed operation. This multi-objective optimization problem is solved by using a fuzzy optimization algorithm which incorporates a finite element method. The torque performance of the motor for various speed ranges is investigated and the optimally designed motor show a better performance at high speed.

• Transactions of The Korea Fluid Power Systems Society
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• v.7 no.1
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• pp.11-19
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• 2010

A gas-assisted hydraulic breaker uses both hydraulic and pneumatic energies and the appropriate balance between them mostly effects its performance. Mathematical modeling of the breaker is established and verified by experiment. Through sensitivity analysis using AMESim, the key design parameters are selected, which mostly affect the performance of the breaker. Taguchi method is used to optimize the key design parameters to maximize the output power for long and short strokes through simulation. As the result, the output power as well as the impact energy are increased significantly compared with the existing design. The pressure pulsation in the supply line is reduced to a tolerable level and the dynamic characteristics of the piston displacement is also improved by the optimization.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.12
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• pp.645-653
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• 2017

Thermal and flow modeling and fin structure optimization were performed to reduce the weight of an electrical device with a staggered fin. First, a numerical model for thermal and flow characteristics was suggested, and then, the model was verified experimentally. Using the verified model, improvement in cooling performance of the cooling system through the staggered fins was predicted. As a result, 87.5% of total heat generated was dissipated through the cooling fins, and a thermal island was observed in the rotor because of low velocity of the internal air flow through the air gap. In addition, it was confirmed that the staggered fin improves the cooling performance but it also increases the total pressure drop within the cooling system, by maximizing the leading edge effect. Based on this analysis result, the effect of each design parameter on the thermal and flow characteristics was analyzed to select the main optimal design parameters, and multi-objective optimization was performed by considering the cooling performance and the fin weight. In conclusion, the optimized fin structure improved the cooling performance by 7% and reduced the fin weight by 28% without any compromise of the pressure drop.