• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.324-329
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• 2005

Dynamic Encoding Algorithm for Searches (DEAS) which is known as a fast and reliable non-gradient optimization method, was proposed [1]. DEAS reaches local or global optimum with binary strings (or binary matrices for multi-dimensional problem) by iterating the two operations; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., 0 or 1), while UDS performs increment or decrement of binary strings in the BSS' result direction with no change of string length. Because the interval of UDS exponentially decreases with increment of bit string length (BSL), DEAS is difficult to escape from local optimum when DEAS falls into local optimum. Therefore, this paper proposes hopping UDS (HUDS) which performs UDS by hopping as many as BSL in the final point of UDS process. HUDS helps to escape from local optimum and enhances a probability searching global optimization. The excellent performance of HUDS will be validated through the well-known benchmark functions.

• 정보처리학회논문지B
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• 제10B권3호
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• pp.237-242
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• 2003

Ant Colony System(ACS) 알고리즘은 조합 최적화 문제를 해결하기 위한 메타 휴리스틱 탐색 방법이다. 이것은 greedy search뿐만 아니라 exploitation of positive feedback을 사용한 모집단에 근거한 접근법으로 Traveling Salesman Problem(TSP)를 풀기 위해 제안되었다. 본 논문에서는 전통적 전역갱신과 지역갱신 방법에 개미들이 방문한 각 간선에 대한 방문 횟수를 강화값으로 추가한 새로운 방법의 ACS를 제안한다. 그리고 여러 조건 하에서 TCS 문제를 풀어보고 그 성능에 대해 기존의 ACS 방법과 제안된 ACS 방법을 비교 평가해, 최적해에 더 빨리 수렴함을 실험을 통해 알 수 있었다.

• 전기학회논문지
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• 제58권4호
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• pp.840-848
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• 2009

This paper proposes a new multi-start scheme that generates guided random bits in selecting initial search points for global optimization with univariate dynamic encoding algorithm for searches (uDEAS). The proposed method counts the number of 1 in each bit position from all the previously generated initial search matrices and, based on this information, generates 0 in proportion with the probability of selecting 1. This rule is simple and effective for improving diversity of initial search points. The performance improvement of the proposed multi-start is validated through implementation in uDEAS and function optimization experiments.

• Structural Monitoring and Maintenance
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• 제1권4호
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.672-676
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• 2003

Recently, the importance of ship vibration is emerging due to the large scaling, high speed and lightning of ship. For pleasantness in a cabin, shipbuilders ask for strict vibration criteria and the degree of vibration level at a deckhouse became an important condition for taking order from customers. This study conducted optimum design to attenuate vibration level of a deckhouse to solve above problems. New method was implemented, that is NASTRAN external call type independence optimization method. The merit of this method is global searching after setting various object functions and design variables. The global optimization algorithm used here is R-Tabu search method, which has fast converging time and searching various size domains. By modeling similar type to ship structure, validity of the suggested method was investigated.

• 한국자동차공학회논문집
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• 제12권6호
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• pp.135-142
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• 2004

The severe frontal crash tests are NCAP with belted occupant at 35mph and FMVSS 208 with unbelted occupant at 25mph, This paper describes the design process of occupant protection systems, airbag and seat belt, under the two tests. In this study, NCAP simulations are performed by Monte Carlo search method and cluster analysis. The Monte Carlo search method is a global optimization technique and requires execution of a series of deterministic analyses, The procedure is as follows. 1) Define the region of interest 2) Perform Monte Carlo simulation with uniform distribution 3) Transform output to obtain points grouped around the local minima 4) Perform cluster analysis to obtain groups that are close to each other 5) Define the several feasible design ranges. The several feasible designs are acquired and checked under FMVSS 208 simulation with unbelted occupant at 25mph.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 A
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• pp.403-405
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• 2003

In this paper, we applied a chaos search method for feeder reconfiguration problem in unbalanced distribution system. Chaos method, in optimization problem, searches the global optimal solution on the regularity of chaotic motions and more easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos search method applied to the IEEE 13 unbalanced test feeder systems, and the test results indicate that it is able to determine appropriate switching options for global optimum configuration.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.1035-1040
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• 2004

In the proto design stage of a new car, the performance of an occupant protection system is often evaluated by CAE instead of the real test. CAE predicts and recommends the appropriate design values hence reducing the number of the real tests. However, the existing researches using CAE in predicting the performances do not consider the uncertainties of parameters, in which inconsistency between the actual test results and CAE exists. In this research, the optimization procedure of a protection system such as airbag and load limiter is suggested for the frontal collision. The DACE modeling known as Kriging interpolation is introduced to obtain the meta model of the system followed by the tabu search method to determine a global optimum. Finally, the distribution of a suggested design is determined through the Monte-Carlo Simulation.

• 전자공학회논문지A
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• 제32A권4호
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• pp.93-102
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• 1995

This paper introduces a general purpose Associative Processor(AP) which is very efficient for search-oriented applications. The proposed architecture consists of three main functional blocks: Content-Addressable Memory(CAM) arry, row logic, and control section. The proposed AP is a Single-Instruction, Multiple-Data(SIMD) device based on a CAM core and an array of high speed processors. As an application for the proposed hardware, we present a parallel algorithm to solve a global routing problem in the layout process utilizing the processing capabilities of a rudimentary logic and the selective matching and writing capability of CAMs, along with basic algorithms such a minimum(maximum) search, less(greater) than search and parallel arithmetic. We have focused on the simultaneous minimization of the desity of the channels and the wire length by sedking a less crowded channel with shorter wire distance. We present an efficient mapping technique of the problem into the CAM structure. Experimental results on difficult examples, on randomly generated data, and on benchmark problems from MCNC are included.

• Journal of information and communication convergence engineering
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• 제13권2호
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• pp.123-131
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• 2015

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input-output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.