• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.57-65
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• 2015

Manufacturing optimization problem is to find the optimal solution under satisfying various and complicated constraints with the design variables of nonlinear types. To achieve the objective, this paper proposes a hybrid approach. The proposed hybrid approach is consist of genetic algorithm(GA), cuckoo search(CS) and hill climbing method(HCM). First, the GA is used for global search. Secondly, the CS is adapted to overcome the weakness of GA search. Lastly, the HCM is applied to search precisely the convergence space after the GA and CS search. In experimental comparison, various types of manufacturing optimization problems are used for comparing the efficiency between the proposed hybrid approach and other conventional competing approaches using various measures of performance. The experimental result shows that the proposed hybrid approach outperforms the other conventional competing approaches.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.121-135
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• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.209-209
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• 2015

상수도 관망은 대표적인 사회기반시설로 수원으로부터 수용가에 이르기까지 안정적으로 유량을 공급하는 것을 목표로 한다. 상수도 관망의 최적설계는 요구되는 절점의 수압, 관로의 유속 등 수리학적 제약조건을 만족시키는 범위 안에서 비용을 최소화하는 설계안을 얻어내는 것을 목표로 시작하였다. 하지만 비용만을 고려한 과거의 상수도 관망 최적설계는 미래의 불확실한 조건에 매우 취약하고, 사용자의 다양한 요구를 충족시키지 못한다. 이 때문에 현대의 상수도 관망의 설계시 다양한 설계인자의 고려와 함께 효율적인 최적설계기법 적용의 필요성이 대두되고 있다. 따라서 본 연구에서는 상수도 관망 최적설계에 다양한 설계인자를 동시에 고려하기 위해 다목적 최적 설계기법인 Multi-objective Harmony Search 알고리즘을 적용하였다. 또한 다목적 최적설계의 효율성 증대를 위하여 매개변수 자동보정 기법 중 하나인 Parameter-Setting-Free (PSF) 기법(Geem and Sim, 2010)을 사용하였다. PSF 기법은 최적화 알고리즘의 매개변수 설정의 번거로움을 없애고, 반복수행을 통한 해 탐색이 진행됨에 따라 가장 효율적으로 작용하는 매개변수를 자동으로 설정하여 탐색효율을 강화하도록 고안된 기법이다. 본 연구에서는 제안된 기법을 실제 상수도관망의 최적설계에 적용하였고 그 결과를 분석하였다. 그 결과 제안된 기법을 통해 관망의 비용을 포함한 다양한 설계인자를 동시에 만족시키는 최적설계안을 효과적으로 도출 할 수 있었으며, 매개변수 자동보정 기법의 적용을 통해 해 탐색의 효율성과 편의성을 향상시킬 수 있었다.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.177-187
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• 2006

It is known that over one million car navigation devices are being currently used in Korea. Most. if not all, route guidance systems, however, Provide only one "best" route to users, not providing any options for various types of users to select. The current practice dose not consider each individual's different preferences. These days, a vast amount of information became available due to the rapid development in information processing technology. Thus, users Prefer choices to be given and like to select the one that suits him/her the "best" among available information. To provide such options in this Paper, we developed an algorithm that provides alternative routes that may not the "least cost" ones, but ones that are close to the "least cost" routes for users to select. The algorithm developed and introduced in the paper utilizes a link-based search method, rather than the traditional node-based search method. The link-based algorithm can still utilize the existing transportation network without any modifications, and yet enables to provide flexible route guidance to meet the various needs of users by allowing transfer to other modes and/or restricting left turns. The algorithm developed has been applied to a toy network and demonstrated successful implementation of the multi-path search algorithm for multi-purpose activities.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.53-62
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• 2004

This paper presents a study on the optimization for brokering between cargos and ships for future e-logistics. The primary contribution of this research is that we establish an optimization model by formalizing the criteria for the brokering such as time constraints, weight constraints, and preference values between cargos and ships. Another important contribution is that we not only investigate the complexity and the tractability of the optimal brokering problem but present how to evaluate the performance of the optimization program through an experiment. We first derive the preference values between cargos and ships using the time and the weight constraints. These preference values between each pair of cargos and ships are assigned to corresponding binary decision variables as coefficients in the objective function. The optimization model selects pairs of cargos and ships in a way that the sum of the preference values is maximized while satisfying given constraints. Experiment shows that the Davis-Putnam based optimization program finds optimal solutions in reasonable time for the problems with less than 90 decision variables.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.8
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• pp.24-32
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• 2006

Using a deterministic design optimization, the effect of uncertainty can result in violation of constraints and deterioration of performances. For this reason, design optimization is required to guarantee reliability for constraints and ensure robustness for an objective function under uncertainty. Therefore, this study drew Monte Carlo Simulation(MCS) for the evaluation of reliability and robustness, and selected an artificial neural network as an approximate model that is suitable for MCS. Applying to the aero-structural optimization problem of aircraft wing, we can explore robuster optima satisfying the sigma level of reliability than the baseline.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1323-1328
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• 2011

In the current design process for the lower arm used in automobile parts, an optimal solution of its various design variables should be found through exploration of the design space approximated using the response surface model formulated with a first- or second-order polynomial equation. In this study, a multi-level computational DOE (design of experiment) was carried out to explore the design space showing nonlinear behavior, in terms of factors such as the total weight and applied stress of the lower arm, where a fractional-factorial orthogonal array based on the artificial neural network model was introduced. In addition, the tolerance robustness of the optimal solution was estimated using a tolerance optimization with six sigma constraints, taking into account the tolerances occurring in the design variables.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.3
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• pp.35-44
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• 2001

This paper presents a loop-based genetic algorithm for loss minimization of distribution systems by automatic sectionalizing switch operation in distribution systems. Genetic algorithm can be successfully applied to problem of loss minimization in distribution systems because it is suitable to solve combinatorial optimization problems. New loop-based string structure is proposed for generating the more feasible solutions, and the proposed restoration function converts infeasible solutions into feasible solutions. The loop-based genetic algorithm with sam adaptations have been applied to improve the computation time and convergence property. Numerical examples demonstrate the validity and effectiveness of the proposed methodology using a 32-bus and 69-bus system.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.700-705
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• 2014

An anagram is a form of word play to find a new word from a set of given alphabet letters. Good human anagram solvers use the strategy of bigrams. They explore a constraint satisfaction network in parallel and answers consequently pop out quickly. In this paper, we propose a molecular computational algorithm using the same process as this. We encoded letters into DNA sequences and made bigrams and then words by connecting the letter sequences. From letters and bigrams, we performed DNA hybridization, ligation, gel electrophoresis and finally, extraction and separation to extract bigrams. From the matched bigrams and words, we performed the four molecular operations again to distinguish between right and wrong results. Experimental results show that our molecular computer can identify cor rect answers and incorrect answers. Our work shows a new possibility for modeling the cognitive and parallel thinking process of a human.

• Smart Media Journal
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• v.12 no.4
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• pp.19-26
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• 2023

IoT edge services utilizing neuromorphic hardware architectures are suitable for autonomous IoT applications as they perform intelligent processing on the device itself. However, spiking neural networks applied to neuromorphic hardware are difficult for IoT developers to comprehend due to their complex structures and various hyper-parameters. In this paper, we propose a method for generating spiking neural network (SNN) models that satisfy user performance requirements while considering the constraints of neuromorphic hardware. Our proposed method utilizes previously trained models from pre-processed data to find optimal SNN model parameters from profiling data. Comparing our method to a naive search method, both methods satisfy user requirements, but our proposed method shows better performance in terms of runtime. Additionally, even if the constraints of new hardware are not clearly known, the proposed method can provide high scalability by utilizing the profiled data of the hardware.