• 제어로봇시스템학회논문지
• /
• 제13권8호
• /
• pp.790-797
• /
• 2007

This paper proposes a new measure of cost function ruggedness in local optimization with DEAS. DEAS is a computational optimization method developed since 2002 and has been applied to various engineering fields with success. Since DEAS is a recent optimization method which is rarely introduced in Korean, this paper first provides a brief overview and description of DEAS. In minimizing cost function with this non-gradient method, information on function shape measured automatically will enhance search capability. Considering the search strategies of DEAS are well designed with binary matrix structures, analysis of search behaviors will produce beneficial shape information. This paper deals with a simple quadratic function contained with various magnitudes of noise, and DEAS finds local minimum yielding ruggedness measure of given cost function. The proposed shape information will be directly used in improving DEAS performance in future work.

• International Journal of Control, Automation, and Systems
• /
• 제6권4호
• /
• pp.571-582
• /
• 2008

This paper analyzes global convergence of the univariate dynamic encoding algorithm for searches (uDEAS) and provides an application result to function optimization. uDEAS is a more advanced optimization method than its predecessor in terms of the number of neighborhood points. This improvement should be validated through mathematical analysis for further research and application. Since uDEAS can be categorized into the generating set search method also established recently, the global convergence property of uDEAS is proved in the context of the direct search method. To show the strong performance of uDEAS, the global minima of four 30 dimensional benchmark functions are attempted to be located by uDEAS and the other direct search methods. The proof of global convergence and the successful optimization result guarantee that uDEAS is a reliable and effective global optimization method.

• 전기학회논문지
• /
• 제58권2호
• /
• pp.382-390
• /
• 2009

This paper newly proposes a modular type dynamic encoding algorithm for searches (DEAS) which partitions the whole parameters into several modules and carries out exhaustive DEAS for each module. uDEAS is used to measure parameter sensitivities to the cost function, and the variables whose sensitivities are similar are grouped to make a module. The proposed optimization method is applied to optimal trajectory generation for biped walking of a humanoid. and the optimization result is compared with those of the former versions of DEAS.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.857-862
• /
• 2004

This paper analyzes properties of the recently developed nonlinear optimization method, Dynamic Encoding Algorithm for Searches (DEAS) [1]. DEAS locates local minima with binary strings (or binary matrices for multi-dimensional problems) by iterating the two operators; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., zero or one), while UDS performs increment or decrement to binary strings with no change of string length. Owing to these search routines, DEAS retains the optimization capability that combines the special features of several conventional optimization methods. In this paper, a special feature of BSS and UDS in DEAS is analyzed. In addition, a effective global search strategy is established by using information of DEAS. Effectiveness of the proposed global search strategy is validated through the well-known benchmark functions.

• 전기학회논문지
• /
• 제56권6호
• /
• pp.1055-1063
• /
• 2007

This paper introduces an optimal design technique for a 250-watt isolation transformer using an optimization method, dynamic encoding algorithm for searches (DEAS). Although the optimal design technique for transformers dates back to 1970s and various optimization methods have been developed so far, literature concerning global optimization for transformer core design is rarely found against its importance. In this paper, core configuration of the isolation transformer whose performance is computed by complex mathematical steps is optimized with DEAS. The optimization result confirms that DEAS can be successfully employed to transformer core design for various performance specifications only by adjusting weight factors in cost function.

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

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

• 조명전기설비학회논문지
• /
• 제22권10호
• /
• pp.24-33
• /
• 2008

본 논문에서는 유한요소법(Finite Element Method)을 기반으로 하는 직접 구동형 영구자석 풍력발전기를 DEAS(Dynamic Encoding Algorithm for Searches)를 이용하여 연간 최대에너지 생산량(Annual Energy Production : AEP) 최대화를 목표로 최적설계 하였다. 특히, 풍력발전기의 전 운전영역을 고려하기 위하여 해당풍속에서의 통계적 확률밀도와 연간 운전시간을 적용하여 연간 최대에너지 생산량을 산정 하였으며, 여기서 발생한 과도한 해석수행 연산시간을 줄이기 위해서 전역 최적화 알고리즘인 DEAS를 적용하여 풍력발전기 최적설계를 수행하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2008년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
• /
• pp.59-61
• /
• 2008

Optimal design of the direct-driven PM Wind Generator, combined with DEAS(Dynamic Encoding Algorithm for Searches) and FEM(Finite Element Method), has been proposed to maximize the Annual Energy Production(AEP) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, DEAS has been contributed to reducing the excessive computing time for the optimization process.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.111-113
• /
• 2006

DEAS is an direct searching and optimization method that based on the binary code space. It can be classified as an direct hill climbing searching. However, because of binary code space based searching, the searching in low resolution has random property. As the resolution of code increases during the search, its property of searching changes like that of hill climbing search. This paper propose a method for improving the performance of minimum seeking ability of DEAS with history information. The cost evaluation is increased. However the minimum searching ability of DEAS is improved along the same starting resolution.