• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.595-602
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• 2003

This Paper presents an efficient hybrid techniques for a standard cell placement. The prototype tool adopts a middle-down methodology in which an n${\times}$m grid is imposed over the layout area and cells are assigned to bins forming a global placement. The optimization technique applied in this phase is based on the Relaxation-Based Local Search (RBLS) framework [12］in which a combinatorial search mechanism is driven by an analytical engine. This enables a more global view of the problem and results in complex modifications of the placement in a single search“move.”Details of this approach including a novel placement legalization procedure are presented. When a global placement converges, a detailed placement is formed and further optimized by the optimal interleaving technique[13］. Experimental results on MCNC benchmarking circuits are presented and compared with the Feng Shui's results in［14］. Solution Qualifies are almost the same as the Feng Shui's results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1057-1064
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• 2009

This paper introduces a new approach based on a quantum-inspired evolutionary algorithm (QEA) to solve unit commitment (UC) problems. The UC problem is a complicated nonlinear and mixed-integer combinatorial optimization problem with heavy constraints. This paper proposes a bounded quantum evolutionary algorithm (BQEA) to effectively solve the UC problems. The proposed BQEA adopts both the bounded rotation gate, which is simplified and improved to prevent premature convergence and increase the global search ability, and the increasing rotation angle approach to improve the search performance of the conventional QEA. Furthermore, it includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in the UC problems. Since the excessive spinning reserve can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BQEA, it is applied to the large-scale power systems of up to 100-unit with 24-hour demand.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1309-1314
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• 2007

The assignment of cells to reporting or non-reporting cells is an NP-hard problem having an exponential complexity in the Reporting Cell Location Management (RCLM) system. Frequent location update may result in degradation of quality of service due to interference. Miss on the location of a mobile terminal will necessitate a search operation on the network when a call comes in. The number of reporting cells and which cell must be reporting cell should be determined to balance the registration (location update) and search (paging) operations to minimize the cost of RCLM system. T1is paper compares Max-Min ant system (MMAS), rank-based ant system (RAS) and hybrid method of MMAS and RAS that generally used to solve combinatorial optimization problems. Experimental results demonstrate that hybrid method of MMAS and RAS is an effective and competitive approach in fairly satisfactory results with respect to solution quality and execution time for the optimal design of location management system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.15-19
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• 2004

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the number of visiting times and the distance between visited cities. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.55-64
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• 2023

The travelling salesman problem (TSP) is an important combinatorial optimization problem that is used in several engineering science branches and has drawn interest to several researchers and scientists. In this problem, a salesman from an arbitrary node, called the warehouse, starts moving and returns to the warehouse after visiting n clients, given that each client is visited only once. The objective in this problem is to find the route with the least cost to the salesman. In this study, a meta-based ant colony system algorithm (ACSA) is suggested to find solution to the TSP that does not use local pheromone update. This algorithm uses the global pheromone update and new heuristic information. Further, pheromone evaporation coefficients are used in search space of the problem as diversification. This modification allows the algorithm to escape local optimization points as much as possible. In addition, 3-opt local search is used as an intensification mechanism for more quality. The effectiveness of the suggested algorithm is assessed on a several standard problem instances. The results show the power of the suggested algorithm which could find quality solutions with a small gap, between obtained solution and optimal solution, of 1%. Additionally, the results in contrast with other algorithms show the appropriate quality of competitiveness of our proposed ACSA.

• Structural Engineering and Mechanics
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• v.47 no.4
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• pp.513-530
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• 2013

In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.

• ETRI Journal
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• v.29 no.3
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• pp.336-352
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• 2007

Sequential pattern mining has become an essential task with broad applications. Most sequential pattern mining algorithms use a minimum support threshold to prune the combinatorial search space. This strategy provides basic pruning; however, it cannot mine correlated sequential patterns with similar support and/or weight levels. If the minimum support is low, many spurious patterns having items with different support levels are found; if the minimum support is high, meaningful sequential patterns with low support levels may be missed. We present a new algorithm, weighted interesting sequential (WIS) pattern mining based on a pattern growth method in which new measures, sequential s-confidence and w-confidence, are suggested. Using these measures, weighted interesting sequential patterns with similar levels of support and/or weight are mined. The WIS algorithm gives a balance between the measures of support and weight, and considers correlation between items within sequential patterns. A performance analysis shows that WIS is efficient and scalable in weighted sequential pattern mining.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.196-197
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• 2015

In tall building construction, time for transporting form affects formwork duration as plan size has become larger and quantity of inputted form has been increased. Thus, necessity of systematic dismantlement sequence of form has been increased to reduce the duration of formwork. Tabu search has been efficiently applied to solve problem of combinatorial optimization by using tabu list which can improve combination values. Therefore, this study proposes optimization model of dismantlement sequence of table form which has been preferred in tall building construction, to reduce the formwork duration by minimizing time for transporting form.

• Journal of the Korea Computer Graphics Society
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• v.1 no.2
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• pp.254-261
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• 1995

지금까지 솔리드의 인식이나 복원 문제는 단면이나 삼면도에서의 다면체에 관한 것들이 대부분이다. 본 연구에서는 2면도로 표현된 도면을 이용하여 3차윈 솔리드 모델로 자동 복원하는 알고리즘의 제안으로, 주어진 2면도를 해석하고 이 2면도를 만족하면서 물체의 구성 조건에 모순되지 않는 입체 모델을 생성하는 방법에 관해서 논한다. 본 논문의 특징은, 첫째 두 면도간의 정합성을 고려하는 규칙(rule)을 도입하여 후보면의 수를 줄이고, 둘째, 면의 combinatorial search과정에서 면의 결정 규칙을 도입하여 탐색 후보면을 솔리드 생성에 사용되는 면과 사용하지 않는 허물체 요소로 미리 구분하는 과정을 통해 탐색 공간의 축소와 탐색 시간의 효율화를 이루는 것이다. 실험을 통하여 이 방법에 대한 유효성과 타당성을 확인한다.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.10-19
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• 2005

The minimum spanning tree (MST) problem is one of the traditional optimization problems. Unlike the MST, the degree constrained minimum spanning tree (DCMST) of a graph cannot, in general, be found using a polynomial time algorithm. So, finding the DCMST of a graph is a well-known NP-hard problem of importance in communications network design, road network design and other network-related problems. So, it seems to be natural to use evolutionary algorithms for solving DCMST. Especially, when applying an evolutionary algorithm to spanning tree problems, a representation and search operators should be considered simultaneously. This paper introduces a new tree representation scheme and a genetic operator for solving combinatorial tree problem using evolutionary algorithms. We performed empirical comparisons with other tree representations on several test instances and could confirm that the proposed method is superior to other tree representations. Even it is superior to edge set representation which is known as the best algorithm.