• 대한전자공학회논문지
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• 제24권6호
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• pp.1068-1073
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• 1987

Traditional circuit partitioning algorithm using the cluster development method, which is suitable for such applications as single chip floor planning or multiple layer PCB system placement, where the clusters are formed so that inter-cluster nets are localized within the I/O connector pins, may not be appropriate for the functiona block placement in truly 3-D electronic modules. 3-D hybrid IC is one such example where the inter-layer routing as well as the intra-layer routing can be maximally incorporated to reduce the overall circuit size, cooling requirements and to improve the speed performance. In this paper, we propose a new algorithm called MBE(Minimum Box Embedding) for the layer assignment of each functional block in 3-D hybrid IC design. The sequence of MBE is as follows` i) force-directed relaxation in 3-D space, ii) exhaustive search for the optimal orientation of the slicing plane and iii) layer assignment. The algorithm is first explaines for a 2-D reduced problem, and then extended for 3-D applications. An example result for a circuit consisting of 80 blocks has been shown.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.126-129
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• 1997

This paper propose a new hybrid genetic algorithm for auto-tunig auzzy controller improving the performance. In general, fuzzy controller used pre-determine d moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controller, using hybrid genetic algorithms. The object of the proposed algorithm is to promote search efficiency by overcoming a premature convergence of genetic algorithms. Hybrid genetic algorithm is based on genetic algorithm and modified gradient method. Simulation results verify the validity of the presented method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.157-159
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• 2007

The conventional hard disk has been the dominant database storage system for over 25 years. Recently, hybrid systems which incorporate the advantages of flash memory into the conventional hard disks are considered to be the next dominant storage systems to support databases for desktops and server computers. Their features are satisfying the requirements like enhanced data I/O, energy consumption and reduced boot time, and they are sufficient to hybrid storage systems as major database storages. However, we need to improve traditional index node management schemes based on B-Tree due to the relatively slow characteristics of hard disk operations, as compared to flash memory. In order to achieve this goal, we propose a new index node management scheme called FNC-Tree. FNC-Tree-based index node management enhanced search and update performance by caching data objects in unused free area of flash leaf nodes to reduce slow hard disk I/Os in index access processes.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.469-475
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• 2008

The design of a scramjet inlet is a process to search global optimization results among those factors influencing the geometry of scramjet in their ranges for some requirements. An optimization algorithm of hybrid genetic algorithm based on genetic algorithm and simplex algorithm was established for this purpose. With the sample provided by a uniform method, the compressive angles which also are wedge angles of the inlet were chosen as the inlet design variables, and the drag coefficient, total pressure recovery coefficient, pressure rising ratio and the combination of these three variables are designed specifically as different optimization objects. The contrasts of these four optimization results show that the hybrid genetic algorithm developed in this paper can capably implement the optimization process effectively for the inlet design and demonstrate some good adaptability.

• ETRI Journal
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• 제45권3호
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• pp.418-432
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• 2023

In this work, we proposed a low-complexity hybrid layered tabu-likelihood ascent search (LTLAS) algorithm for large multiple-input multiple-output (MIMO) system. The conventional layered tabu search (LTS) approach involves many partial reactive tabu searches (RTSs), and each RTS requires an initialization and searching phase. In the proposed algorithm, we restricted the upper limit of the number of RTS operations. Once RTS operations exceed the limit, RTS will be replaced by low-complexity likelihood ascent search (LAS) operations. The block-based detection approach is considered to maintain a higher signal-to-noise ratio (SNR) detection performance. An efficient precomputation technique is derived, which can suppress redundant computations. The simulation results show that the bit error rate (BER) performance of the proposed detection method is close to the conventional LTS method. The complexity analysis shows that the proposed method has significantly lower computational complexity than conventional methods. Also, the proposed method can reduce almost 50% of real operations to achieve a BER of 10^-3.

• 한국컴퓨터그래픽스학회논문지
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• 제7권1호
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• pp.11-17
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• 2001

프랙탈 이미지 코딩은 높은 압축율을 비롯하여 많은 장점을 가지고 있다. 그러나 압축 과정은 일반적으로 domain 블록 pool에 대한 긴 탐색시간으로 효율이 나빠진다. 본 논문에서는 블록 분류와 분산 기반의 탐색을 병용한 domain 블록 pool 탐색 방법을 소개한다. 이 방법은 블록 분류에 대하여 분류 블록의 분산 값은 독립적이라는 사실을 이용한다. 따라서 이 방법은 단순한 분산 기반의 탐색 방법보다 O(number of classes)에 비례하는 탐색 속도향상이 된다. 실험의 결과는 본 방법이 단순히 분산 값을 적용한 탐색 방법과 비교하여 이미지 품질은 거의 그대로 유지하면서 17배 이상의 속도 향상을 이루었음을 보인다. 또한 이미지 품질의 가시적인 손실 없이 탐색 속도를 더욱 향상시키는 분산 기반의 탐색 방법을 제안한다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권6호
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• pp.829-839
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• 2004

정수계획법에 기반 한 기법들은 다양한 승무일정계획 최적화 문제를 해결하는 데 매우 효과적인 것으로 알려져 있다. 그러나 정수계획법은 대상 문제의 제약조건 및 목적함수가 모두 선형적으로 표현되어야만 적용이 가능하다는 단점이 있으며 문제의 규모가 클 경우 과도한 수행 시간과 메모리 자원을 요구하게 된다. 반면 이웃해 탐색 기법과 같은 휴리스틱 탐색 기법은 대상 문제의 제약조건이나 목적함수의 형태에 관계없이 쉽게 적응이 가능하다. 그러나 이웃해 탐색 기법은 복잡한 탐색 공간을 탐색할 경우 국소 최적해에 도달한 후 국소 최적해로부터 쉽게 빠져나오지 못하는 경우가 많다. 본 논문에서는 이웃해 탐색 기법과 정수계획법의 장점을 효과적으로 결합하기 위한 방안을 제시하고 있으며 실제 운행중인 지하철 승무일정계획 문제에 적용해 봄으로써 대규모 승무일정계획 최적화 문제에 성공적으로 적용될 수 있음을 확인하였다.

• 대한전자공학회논문지SD
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• 제42권12호
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• pp.61-70
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• 2005

본 논문에서는 "middle-down" 접근법에 기반한 기존의 표준 셀 배치기인 하이브리드 배치기$^{[25]}$의 단점을 보완한 효율적인 광역배치 알고리즘을 제안한다. hMETIS(클러스터링을 이용한 다단계 하이퍼그래프 분할기법)에 사용된 기법과 RBLS(Relaxation Based Local Search) 기법의 적절한 조합을 통해 기존 하이브리드 배치기의 광역배치 기능을 향상시킨다. hMETIS를 통한 분할기법을 "top-down" 방식으로 적용하고, 각 단계에서 RBLS를 사용하여 광역배치를 점진적으로 개선해 나가는 제안된 기법은 초기 배치에 크게 영향을 받는 기존 방법의 문제점을 해결하고, 실행 속도를 개선하면서도 배치의 질을 떨어뜨리지 않는 효과적인 기법이다. 제안한 알고리즘을 통해 구현된 개선된 배치기는 기존의 하이브리드 배치기나 FengShui와 같은 우수한 툴과 비교할 때 뒤지지 않는 성능을 보인다. 특별히 기존의 하이브리드 배치기에 비해 실행 속도 면에서 평균 5배 정도의 개선을 보였고, 큰 회로에 대해선 배선길이도 줄어드는 향상된 결과를 보였다.

• Journal of Advanced Marine Engineering and Technology
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• 제40권2호
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Journal of Communications and Networks
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• 제16권4호
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• pp.430-435
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• 2014

The performance of a distributed database system depends particularly on the site-allocation of the fragments. Queries access different fragments among the sites, and an originating site exists for each query. A data allocation algorithm should distribute the fragments to minimize the transfer and settlement costs of executing the query plans. The primary cost for a data allocation algorithm is the cost of the data transmission across the network. The data allocation problem in a distributed database is NP-complete, and scalable evolutionary algorithms were developed to minimize the execution costs of the query plans. In this paper, quadratic assignment problem heuristics were designed and implemented for the data allocation problem. The proposed algorithms find near-optimal solutions for the data allocation problem. In addition to the fast ant colony, robust tabu search, and genetic algorithm solutions to this problem, we propose a fast and scalable hybrid genetic multi-start tabu search algorithm that outperforms the other well-known heuristics in terms of execution time and solution quality.