• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.183-186
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• 2003

Hybrid genetic algorithms (HGAs) have been studied as various ways. These HGAs usually use both the global search property of genetic algorithm (GA) and the local search one of local search techniques. One of the general types, when constructing HGAs, is to incorporate a local search technique into GA loop, and then the local search technique is repeated as many iteration number as the loop. This paper proposes a new HGA with a conditional local search technique (c-HGA) that does not be repeated as many iteration number as GA loop. For effectiveness of the proposed c-HGA, a conventional HGA and GA are also suggested, and then these algorithms are compared with each other in numerical examples,

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2097-2098
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• 2006

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.1-15
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• 1998

Genetic algorithms(GAs) are suited for solving structural design problems, since they handle the design variables efficiently. This ability of GAs considers then as a good choice for optimization problems. Nevertheless, there are many situations that the conventional genetic algorithms do not perform particularly well, and so various methods of hybridization have been proposed. Thus. this paper develops a hybrid genetic algorithm(HGA) to incorporate a local convergence method and precision search method around optimum in the genetic algorithms. In case study. it is showed that HGA is able consistently to provide efficient, fine quality solutions and provide a significant capability for solving structural design problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.203-210
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• 2002

The great advantages on the Genetic Algorithms(GAs) are ease of implementation, and robustness in solving a wide variety of problems, several GAs based optimization models for solving complex structural problems were proposed. However, there are two major disadvantages in GAs. The first disadvantage, implementation of GAs-based optimization is computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. The second problem is too difficult to find proper parameter for particular problem. Therefore, in this paper, a Distributed Hybrid Genetic Algorithms(DHGAs) is developed for structural optimization on a cluster of personal computers. The algorithm is applied to the minimum weight design of steel structures.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.274-281
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• 2013

In this study, we consider the challenges in motion planning for automated driving systems. Most of the existing online motion-planning algorithms, which take dynamics into account, find it difficult to operate in an environment with narrow passages. Some of the existing algorithms overcome this by offline preprocessing if environment is known. In this work an online algorithm for motion planning with dynamics in an unknown cluttered environment with narrow passages is presented. It utilizes an idea of hybrid planning with sampling- and discretization-based motion planners, which run simultaneously in a full configuration space and a derived reduced space. The proposed algorithm has been implemented and tested with a real autonomous vehicle. It provides significant improvements in computational time performance over basic planning algorithms and allows the generation of smoother paths than those generated by the recently developed hybrid motion planners.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.442-449
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• 2003

This paper presents a robust hybrid control system for seismic response control of a cable-stayed bridge. Because multiple control devices are operating, a hybrid control system could alleviate some of restirctions and limitations that exist when each system is acting alone. A LQG algorithm with on-off control scheme, H$_2$ and H$_{\infty}$ control algorithms with various frequency weighting filters are used to improve the controller robustness of the active control part in the hybrid control system. The numerital simulation results show that control performances of robust hybrid control systems are similar to those of the hybrid control system with LQG algorithm. Furthermore, it is verified that robust hybrid control systems are more robust than the hybrid control system with LQG algorithm and there are no signs of instabilities in the $\pm$5％ stiffness matrix perturbed system. Therefore, the proposed hybrid control system have a good robustness for stiffness matrix perturbation without loss of control effectiveness.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.475-484
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• 2021

The optimal design for water distribution system (WDS) is not only satisfying the minimum required water pressure of the nodes, but also minimizing pipe cost, etc. The number of designs of WDS increases exponentially due to the arrangement of various pipes. Various optimization algorithms were applied to propose an optimized design of WDS. In this study, Modified Hybrid Vision Correction Algorithm (MHVCA) with improved self-adapting parameter was applied to optimal design of WDS. The performance was improved by changing the Hybrid Rate (HR) of the existing Hybrid Vision Correction Algorithm (HVCA) to nonlinear HR. To verify the performance of the proposed MHVCA, it applied to mathematical problems consisting of 2 and 30 decision variables and constrained mathematical problems. In order to review the application results of MHVCA, it was compared with Harmony Search (HS), Improved Harmony Search (IHS), Vision Correction Algorithm (VCA) and HVCA. Finally, MHVCA was applied to the optimal design problem of WDS and the results were compared with other algorithms. MHVCA showed better results than other algorithms in mathematical problems and WDS problem. MHVCA will be able to show good results by applying to various water resource engineering problems as well as problems applied in this study.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1257-1264
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• 2018

This paper proposes Autoregressive Integrated Moving Average (ARIMA)-based forecasting algorithms using meteorological indices to predict seasonal peak load. First of all, this paper observes a seasonal pattern of the peak load that appears intensively in winter and summer, and generates ARIMA models to predict the peak load of summer and winter. In addition, this paper also proposes hybrid ARIMA-based models (ARIMA-Hybrid) using a discomfort index and a sensible temperature to enhance the conventional ARIMA model. To verify the proposed algorithm, both ARIMA and ARIMA-Hybrid models are developed based on peak load data obtained from 2006 to 2015 and their forecasting results are compared by using the peak load in 2016. The simulation result indicates that the proposed ARIMA-Hybrid models shows the relatively improved performance than the conventional ARIMA model.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.499-509
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• 2018

Recently, hybrid transactional memory (HyTM) has gained much interest from researchers because it combines the advantages of hardware transactional memory (HTM) and software transactional memory (STM). To provide the concurrency control of transactions, the existing HyTM-based studies use a bloom filter. However, they fail to overcome the typical false positive errors of a bloom filter. Though the existing studies use a global lock, the efficiency of global lock-based memory allocation is significantly low in multi-core environment. In this paper, we propose an efficient hybrid transactional memory scheme using near-optimal retry computation and sophisticated memory management in order to efficiently process transactions in multi-core environment. First, we propose a near-optimal retry computation algorithm that provides an efficient HTM configuration using machine learning algorithms, according to the characteristic of a given workload. Second, we provide an efficient concurrency control for transactions in different environments by using a sophisticated bloom filter. Third, we propose a memory management scheme being optimized for the CPU cache line, in order to provide a fast transaction processing. Finally, it is shown from our performance evaluation that our HyTM scheme achieves up to 2.5 times better performance by using the Stanford transactional applications for multi-processing (STAMP) benchmarks than the state-of-the-art algorithms.