• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.130-145
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• 2004

This paper addresses the integrated problem of process planning and scheduling in FMS (Flexible Manufacturing System). The integration of process planning and scheduling is important for an efficient utilization of manufacturing resources. In this paper, a new method using an artificial intelligent search technique, called asymmetric multileveled symbiotic evolutionary algorithm, is presented to handle the two functions at the same time. Efficient genetic representations and operator schemes are considered. While designing the schemes, we take into account the features specific to each of process planning and scheduling problems. The performance of the proposed algorithm is compared with those of a traditional hierarchical approach and existing evolutionary algorithms. The experimental results show that the proposed algorithm outperforms the compared algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2338-2353
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• 2015

In this paper, the optimal power allocation algorithm that minimizes the aggregate bit error rate (BER) of the secondary user (SU) in a downlink orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) system, while subjecting to the interference power constraint and the transmit power constraint, is investigated under the assumption that the instantaneous channel state information (CSI) of the interference links between the secondary transmitter and the primary receiver, and between the primary transmitter and the secondary receiver is perfectly known. Besides, a suboptimal algorithm with less complexity is also proposed. In order to deal with more practical situations, we further assume that only the channel distribution information (CDI) of the interference links is available and propose heuristic power allocation algorithms based on bisection search method to minimize the aggregate BER under the interference outage constraint and the transmit power constraint. Simulation results are presented to verify the effectiveness of the proposed algorithms.

• Structural Engineering and Mechanics
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• v.24 no.5
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• pp.529-541
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• 2006

This work gives an application of stochastic techniques for the optimization of stiffened plates in vibration. The search strategy consists of substituting, for finite element calculations in the optimization process, an approximate response from a Rayleigh-Ritz method. More precisely, the paper describes the use of a Rayleigh-Ritz method in creating function approximations for use in computationally intensive design optimization based on genetic algorithms. Two applications are presented; their deal with the optimization of stiffeners on plates by varying their positions, in order to maximize some natural frequencies, while having well defined dimensions. In other words, this work gives the fundamental idea of using a Ritz approximation to the response of a plate in vibration instead of finite element analysis.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.573-576
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• 2006

This paper deals with multi-objective optimization problem of finding a set of well-distributed solutions close to the true Pareto optimal solutions. In this paper, we present a two-leveled symbiotic evolutionary algorithm to efficiently solve the problem. Most of the existing multi-objective evolutionary algorithms (MOEAs) operate one population that consists of individuals representing the complete solution to the problem. The proposed algorithm maintains several populations, each of which represents a partial solution to the entire problem, and has a structure with two levels. The parallel search and the structure are intended to improve the capability of searching diverse and good solutions. The performance of the proposed algorithm is compared with those of the existing algorithms in terms of convergence and diversity. The experimental results confirm the effectiveness of the proposed algorithm.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1277-1287
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• 2009

For stratifying skewed populations, the Lavall$\acute{e}$e-Hidiroglou(LH) algorithm is often considered to have a take-all stratum with the largest units and some take-some strata with the middle-size and small units. Related to its iterative nature have been reported some numerical difficulties such as the dependency of the ultimate stratum boundaries to a choice of initial boundaries and the slow convergence to locally-optimum boundaries. The geometric stratification has been recently proposed to provide initial boundaries that can avoid such numerical difficulties in implementing the LH algorithm. Since the geometric stratification does not pursuit the optimization but the equalization of the stratum CVs, the corresponding stratum boundaries may not be (near) optimal. This paper revisits these issues concerning convergence and near-optimality of optimal stratification algorithms using artificial numerical examples. We also discuss the formation of the strata and the sample allocation under the optimization process and some aspects related to discontinuity arisen from the finiteness of both population and sample as well.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.729-737
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• 1996

Laminated composite plates have been applied to aircraft structures because their properties are superior to the conventional materials and the laminates have anisortropic elastic properties. However, it tis diffcult to determine stacking structures using actual design variables for the lack of searching capability of existing optimization technique. GA(generic algorithms) are robust search algorithms based on the mechanics of natural selection and natural genetics. Therefore, this study presents an application of IGA to stiffness and weight optimization design and gives the various stacking structures suitable to constraint conditions.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.1
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• pp.127-135
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• 2009

This paper presents a new technique for corner shape based object retrieval from a database. The proposed feature matrix consists of values obtained through a neighborhood operation of detected corners. This results in a significant small size feature matrix compared to the algorithms using color features and thus is computationally very efficient. The corners have been extracted by finding the intersections of the detected lines found using Hough transform. As the affine transformations preserve the co-linearity of points on a line and their intersection properties, the resulting corner features for image retrieval are robust to affine transformations. Furthermore, the corner features are invariant to noise. It is considered that the proposed algorithm will produce good results in combination with other algorithms in a way of incremental verification for similarity.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.474-478
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• 1999

Genetic algorithm is an efficient search method using principles of natural selection and population genetics. In conventional genetic algorithms, however, the size of gene pool should be increased to insure a convergency. Therefore, many memory spaces and much computation time were needed. Also, since child chromosomes were generated by chromosome crossover and gene mutation, the algorithms have a complex structure. Thus, in this paper, a compact stereo matching algorithm using a population-based incremental teaming based on probability vector is proposed to reduce these problems. The PBIL method is modified for matching environment. Since the Proposed algorithm uses a probability vector and eliminates gene pool, chromosome crossover, and gene mutation, the matching algorithm is simple and the computation load is considerably reduced. Even if the characteristics of images are changed, stable outputs are obtained without the modification of the matching algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1680-1686
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• 1995

GAs, effective stochastic search algorithms based on the model of natural evolution and genetics, have been successfully applied to various optimization problems. When population size is not large, GAs often suffer from the phenomenon of premature convergence in which all chromosomes in the population lose the diversity of genes before they find the optimal solution. In this paper, we propose that a new heuristic that maintains the diversity of genes by adding some chromosomes with random mutation and selective mutation into population during evolution. And population size changes dynamically with supplement of new chromosomes. Experimental results for several test functions show that when population size is rather small and the length of chromosome is not long, this method is effective.