• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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• pp.173-181
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• 2012

The size and layout of user interface components need to be optimally designed in terms of reachability, visibility, clearance, and compatibility in order for efficient and effective use of products. The present study develops an ergonomic design method which optimizes the size and layout of user interface components using adaptive genetic algorithm. The developed design method determines a near-optimal design which maximizes the aggregated score of 4 ergonomic design criteria (reachability, visibility, clearance, and compatibility). The adaptive genetic algorithm used in the present study finds a near-optimum by automatically adjusting the key parameter (probability of mutation) of traditional genetic algorithm according to the characteristic of current solutions. Since the adaptive mechanism partially helps to overcome the local optimality problem, the probability of finding the near-optimum has been substantially improved. To evaluate the effectiveness of the developed design method, the present study applied it to the user interface design for a portable wireless communication radio.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1999.11a
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• pp.167-173
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• 1999

This paper suggests a method to design the deep groove ball bearing for high-load capacity by using a genetic algorithm. The design problem of ball bearings is a typical discrete/continuous optimization problem because the deep groove ball bearing has discrete variables, such as ball size and number of balls. Thus, a genetic algorithm is employed to find the optimum values from a set of discrete design variables. The ranking process is proposed to effectively deal with the constraints in genetic algorithm. Results obtained fer several 63 series deep groove ball bearings demonstrated the effectiveness of the proposed design methodology by showing that the average basic dynamic capacities of optimally designed bearings increase about 9~34% compared with the standard ones.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.290-293
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• 1996

This paper proposes a scheme of initial cluster center selection in FCM algorithm using the genetic algorithms. The FCM algorithm often fails in the search for global optimum because it is local search techniques that search for the optimum by using hill-climbing procedures. To solve this problem, we search for a hypersphere encircling each clusters whose parameters are estimated by the genetic algorithms. Then instead of a randomized initialization for fuzzy partition matrix in FCM algorithm, we initialize each cluster center by the center of a searched hypersphere. Our experimental results show that the proposed initializing scheme has higher probabilities of finding the global or near global optimal solutions than the traditional FCM algorithm.

• IE interfaces
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• v.8 no.1
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• pp.23-30
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• 1995

Optimization in the engineering design is to select the best of many possible design alternatives in a complex design space. One major problem of local minimization algorithm is that they often result in local optima. In this paper, a hybrid method was developed by coupling the genetic algorithm and a traditional direct search method. The proposed method first finds a region for possible global optimum using the genetic algorithm and then searchs for a global optimum using the direct search method. To evaluate the performance of the hybrid method, it was applied to three test problems and a problem of designing corrugate bulkhead of a ship.

• Transactions of the Korean Society of Automotive Engineers
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• v.2 no.6
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• pp.9-17
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• 1994

In this study, a shifting algorithm of CVT was suggested for the two(2) driving modes : (1) power mode and (2) economy mode. Shifting algorithm must be obtained to make the engine run on the optimum operating line for the desired performance of the vehicle. Optimum operating lines of the engine were obtained by connecting the shortest way of the iso-power lines for the power mode and by connecting the shortest way of the BSFC curves for the economy mode. Also dynamic model of CVT vehicle was derived considering the throttle and the brake operation. By using the shifting algorithm and the CVT vehicle model, numerical simulations were performed to estimate the performance of CVT. Simulation results showed that comparing the performance of the conventional 4-speed automatic transmission, acceleration performance of the CVT vehicle was almost same with the AT vehicle for the power mode and the fuel economy of CVT was 14% superior than that of AT for the economy mode.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.6 no.1
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• pp.1-11
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• 2000

In optimum design of packaging container for bulk materials, minimum board area, compression performance and distribution efficiency must be considered. In this study, mathematical models for minimum board area (RMA), compression strength (CS) and maximum compression strength per unit board area (MCSA) of container as algorithm for optimum design of packaging conatiner for bulk materials were developed as follows : RMA=f(V,D), ${\alpha}_{RMA}=f(V,D)$, MCSA=f(V,D), and ${\alpha}_{MCSA}=f(V,D)$. In order to develop these models, compression test according to various dimensions of container and response surface analysis for minimum board area, compression strength, and maximum compression strength per unit board area of container were carried out. In developed models, volume and depth of container were principal independent variables. Through the verified results for these models, optimum design of packaging container on the design conditions and limit conditions was possible. These models might be used in developing optimum design software of packaging container for bulk materials.

• Steel and Composite Structures
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• v.30 no.3
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• pp.293-304
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• 2019

The optimum cost of a reinforced concrete plane and space frames have been found by using the Genetic Algorithm (GA) method. The design procedure is subjected to many constraints controlling the designed sections (beams and columns) based on the standard specifications of the American Concrete Institute ACI Code 2011. The design variables have contained the dimensions of designed sections, reinforced steel and topology through the section. It is obtained from a predetermined database containing all the single reinforced design sections for beam and columns subjected to axial load, uniaxial or biaxial moments. The designed optimum beam sections by using GAs have been unified through MATLAB to satisfy axial, flexural, shear and torsion requirements based on the designed code. The frames' functional cost has contained the cost of concrete and reinforcement of steel in addition to the cost of the frames' formwork. The results have found that limiting the dimensions of the frame's beams with the frame's columns have increased the optimum cost of the structure by 2%, declining the re-analysis of the optimum designed structures through GA.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.8
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• pp.598-608
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• 2002

This paper shows the optimal design methodology for the fluid engine mount by the artificial life algorithm. The design has been commonly modified by trial and error because there is many design parameters that can be varied in order to minimize transmissibility at the desired fundamental resonant and notch frequencies. The application of trial and error method to optimization of the fluid mount is a great work. Many combinations of parameters are possible to give us the desired resonant and notch frequencies, but the question is which combination Provides the lowest resonant peak and notch depth. In this study the enhanced artificial life algorithm is applied to get the desired fundamental resonant and notch frequencies of a fluid mount and to minimize transmissibility at these frequencies. The present hybrid algorithm is the synthesis of and artificial life algorithm with the random tabu (R-tabu) search method. The hybrid algorithm has some advantages, which is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all globa1 optimum solutions. The results show that the performance of the optimized mount compared with the original mount is improved significantly.