• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2897-2907
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• 1998

The production-rule generation from training examples is a hard problem that has large space and many local optimal solutions. Many learning methods are proposed for production-rule generation and genetic algorithms is an alternative learning method. However, traditional genetic algorithms has been known to have an obstacle in converging at the global solution area and show poor efficiency of production-rules generated. In this paper, we propose a production-rule generating method which uses genetic algorithm learning. By analyzing optimal sub-solutions captured by genetic algorithm learning, our method takes advantage of its schema structure and thus generates relatively small rule set.

• Journal of Power Electronics
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• v.16 no.2
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• pp.610-620
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• 2016

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.5
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• pp.425-431
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• 2006

Recently, the government has adjusted the 4th National Master Plan in an effect to achieve balanced national land development. However, the current traffic accident index ranks among the lowest in OECD countries, ranking 25th out of 29 countries. Therefore, this study is aimed at indicating problems with National Expressway and local roads developing a solution by analyzing the problems and suggesting the most appropriate design speed for inter-changes where the traffic accidents occur frequently. With the results, it is to obtain a design speed decision formula at interchange branch points to prevent traffic accidents, secure safe and optimal road conditions and maximize traffic load capability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.7
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• pp.620-628
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• 2016

The homotopy algorithm provides a robust method for determining optimal control, in some cases the global minimum solution, as a continuation parameter is varied gradually to regulate the contributions of the nonlinear terms. In this paper, the Successive Backward Sweep (SBS) method, which is insensitive to initial guess, augmented with a homotopy algorithm is suggested. This approach is effective for highly nonlinear problems such as low-thrust trajectory optimization. Often, these highly nonlinear problems have multiple local minima. In this case, the SBS-homotopy method enables one to steadily seek a global minimum.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.324-333
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• 2013

The purpose of this paper is to improve the efficiency of multi-objective topology optimization using a genetic algorithm (GA) with bar-system representation. We proposed a new GA using an elite initial population obtained from a Solid Isotropic Material with Penalization (SIMP) using a weighted sum method. SIMP with a weighted sum method is one of the most established methods using sensitivity analysis. Although the implementation of the SIMP method is straightforward and computationally effective, it may be difficult to find a complete Pareto-optimal set in a multi-objective optimization problem. In this study, to build a more convergent and diverse global Pareto-optimal set and reduce the GA computational cost, some individuals, with similar topology to the local optimum solution obtained from the SIMP using the weighted sum method, were introduced for the initial population of the GA. The proposed method was applied to a structural topology optimization example and the results of the proposed method were compared with those of the traditional method using standard random initialization for the initial population of the GA.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.234-240
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• 1999

This paper presents a new approach for large-scale generator maintenance scheduling optimizations. The generator preventive maintenance scheduling problems are typical discrete dynamic n-dimensional vector optimization ones with several inequality constraints. The considered objective function to be minimized a subset of{{{{ { R}^{n } }}}} space is the variance (i.g., second-order momentum) of operating reserve margin to levelize risk or reliability during a year. By its nature of the objective function, the optimal solution can only be obtained by enumerating all combinatorial states of each variable, a task which leads to computational explosion in real-world maintenance scheduling problems. This paper proposes a new priority search mechanism based on each generator's discrete sensitivity value which was analytically developed in this study. Unlike the conventional capacity-based priority search, it can prevent the local optimal trap to some extents since it changes dynamically the search tree in each iteration. The proposed method have been applied to two test systems (i.g., one is a sample system with 10 generators and the other is a real-world lage scale power system with 280 generators), and the results anre compared with those of the conventional capacith-based search method and combinatorial optimization method to show the efficiency and effectiveness of the algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.4
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• pp.500-509
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• 1999

In this paper, we consider an optimal routing problem when point-to-point and point-to-multipoint connection traffics are offered in an ATM network. We propose a mathematical model for cost-minimizing configuration of a logical network for a given ATM-based BISDN. Our model is essentially identical to the previous one proposed by Kim(Kim, 1996) which finds a virtual-path configuration where the relevant gains obtainable from the ATM technology such as the statistical multiplexing gain and the switching/control cost-saving gain are optimally traded-off. Unlike the Kim's model, however, ours explicitly considers the VP's QoS(Quality of Service) for more efficient utilization of bandwidth. The problem is a large-scale, nonlinear, and mixed-integer problem. The proposed algorithm is based on the local linearization of equivalent-capacity functions and the relaxation of link capacity constraints. As a result, the problem can be decomposed into moderate-sized shortest path problems, Steiner arborescence problems, and LPs. This fact renders our algorithm a lot faster than the previous nonlinear programming algorithm while the solution quality is maintained, hence application to large-scale network problems.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.304-310
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• 2021

The paper presents problems of optimization of the synthesis of embedded systems, in particular Pareto optimization. The model of such a system for its design for high-level of abstract is based on the classic approach known from the theory of task scheduling, but it is significantly extended, among others, by the characteristics of tasks and resources as well as additional criteria of optimal system in scope structure and operation. The metaheuristic algorithm operating according to this model introduces a new approach to system synthesis, in which parallelism of task scheduling and resources partition is applied. An algorithm based on a genetic approach with simulated annealing and Boltzmann tournaments, avoids local minima and generates optimized solutions. Such a synthesis is based on the implementation of task scheduling, resources identification and partition, allocation of tasks and resources and ultimately on the optimization of the designed system in accordance with the optimization criteria regarding cost of implementation, execution speed of processes and energy consumption by the system during operation. This paper presents examples and results for multi-criteria optimization, based on calculations for specifying non-dominated solutions and indicating a subset of Pareto solutions in the space of all solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4617-4632
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• 2021

IEEE 802.11ax is a protocol being developed for high-density Wireless Local Area Networks (WLAN). Several algorithms have been proposed to improve the level of spatial reuse applied in IEEE 802.11ax. However, these algorithms are tentative and do not specify how to select the transmit power and carrier sense threshold in practice; It is unclear when and why the tuned parameters lead to better network performance. In this paper, we restricted the scale of transmit power tuning to prevent the case of backfire in which spatial reuse will result in transmission failure. If the restrictions cannot be satisfied, spatial reuse will be abandoned. This is why we named the proposed scheme as Arbitration based Spatial Reuse (ASR). We quantified the network performance after spatial reuse, and formulate a corresponding maximum problem whose solution is the optimal carrier sense threshold and transmit power. We verified our theoretical analysis by simulation and compared it with previous studies, and the results show that ASR improves the throughput up to 8.6% compared with 802.11ax. ASR can avoid failure of spatial reuse, while the spatial reuse failure rate of existing schemes can up to 36%. To use the ASR scheme in practice, we investigate the relation between the optimal carrier sense threshold and transmit power. Based on the relations got from ASR, the proposed Relation based Spatial Reuse (RSR) scheme can get a satisfactory performance by using only the interference perceived and the previously found relations.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.359-366
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• 2005

Most engineering optimization are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithm, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the result may depend on the selection of an initial point, and the obtained optimal solution may not necessarily be the global optimum. This paper describes a new harmony search(HS) meta-heuristic algorithm-based approach for structural size optimization problems with continuous design variables. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. Two classical space truss optimization problems are presented to demonstrate the effectiveness and robustness of the HS algorithm. The results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to structural engineering problems than those obtained using current algorithms.