• 인터넷정보학회논문지
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• 제21권3호
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• pp.21-28
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• 2020

Fireworks Algorithm (FWA) is a relatively novel swarm-based metaheuristic algorithm for global optimization. To solve the low-efficient local searching problem and convergence of the FWA, this paper presents a Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator (namely VACUFWA). Firstly, the explosive amplitude is used to adjust improving the convergence speed dynamically. Secondly, Uniform Local Search (ULS) enhances exploitation capability of the FWA. Finally, the ULS and Variable Amplitude Coefficient operator are used in the VACUFWA. The comprehensive experiment carried out on 13 benchmark functions. Its results indicate that the performance of VACUFWA is significantly improved compared with the FWA, Differential Evolution, and Particle Swarm Optimization.

• 한국컴퓨터정보학회논문지
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• 제7권1호
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• pp.48-53
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• 2002

본 논문에서는 Rural Postman Problem(RPP) 해법을 위한 Iterative Local Search(ILS) 알고리즘을 제안한다. ILS 알고리즘은 초기해를 여러 번 생성하여 탐색 시작점을 달리하는 방법으로, 초기해의 설정 방법에 따라 알고리즘의 성능이 크게 좌우되는 Local Search(LS) 알고리즘의 단점을 보완할 수 있다. 본 논문에서는 LS 알고리즘과 ILS 알고리즘을 18개의 RPP 문제에 적용하고 그 성능을 분석한다. 실험 결과에서는 ILS 알고리즘이 각각 다른 탐색 시작점에서 해 공간을 탐색함으로서 LS에 비해 좋은 해를 찾을 수 있음을 알 수 있었다.

• 한국지능시스템학회논문지
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• 제8권3호
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• pp.71-79
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• 1998

본 논문은 유전알고리즘과 random tabu 탐색법을 조합한 새로운 최적화 알고리즘을 제안한다. 유전알고리즘과 전역적인 최적해에 대한 탐색능력이 우수하고, random tabu 탐색법은 최적해에의 수렴속도가 매우 빠른 알고리즘이다. 본 논문에서는 이 두 알고리즘의 장점을 이용해서 수렴정도와 수렴속도가 더욱 향상된 최적알고리즘을 제안하여 알고리즘의 수렴성능을 조사하고, 실제 최적화문제로서 지진응답을 최소로 하기위한 배관지지대의 최적배치문제에 적용하여 기존의 방법과 비교를 통하여 유용성을 검토하였다.

• 산업공학
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• 제18권4호
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• pp.504-511
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• 2005

The NP-hard channel assignment problem becomes more and more important to use channels as efficiently as possible because there is a rapidly growing demand and the number of usable channel is very limited. The hybrid genetic and local search (HGLS) method in this paper is a hybrid method of genetic algorithm with no interference channel assignment (NICA) in clustering stage for diversified search and local search in tuning stage when the step of search is near convergence for minimizing blocking calls. The new representation of solution is also proposed for effective search and computation for channel assignment.

• Journal of applied mathematics & informatics
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• 제26권3_4호
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• pp.493-500
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• 2008

In this paper, we first construct a mathematical model for tagSNP selection based on LD measure $r^2$, then aiming at this kind of model, we develop an efficient algorithm, which is called approximate greedy algorithm. This algorithm is able to make up the disadvantage of the greedy algorithm for tagSNP selection. The key improvement of our approximate algorithm over greedy algorithm lies in that it adds local replacement(or local search) into the greedy search, tagSNP is replaced with the other SNP having greater similarity degree with it, and the local replacement is performed several times for a tagSNP so that it can improve the tagSNP set of the local precinct, thereby improve tagSNP set of whole precinct. The computational results prove that our approximate greedy algorithm can always find more efficient solutions than greedy algorithm, and improve the tagSNP set of whole precinct indeed.

• 인지과학
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• 제4권2호
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• pp.181-200
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• 1994

다층 신경망의 학습에 있어서 역전파 알고리즘은 시스템이 지역적 최소치에 빠질수 있고,탐색공간의 피라미터들에 의해 신경망 시스템의 성능이 크게 좌우된다는 단점이 있다.이러한 단점을 보완하기 의해 유전자 알고리즘이 신경망의 학습에 도입도었다.그러나 유전자 알고리즘에는 역전파 알고리즘과 같은 미세 조정되는 지역적 탐색(fine-tuned local search) 을 위한 메카니즘이 존재하지 않으므로 시스템이 전역적 최적해로 수렴하는데 많은 시간을 필요로 한다는 단점이 있다. 따라서 본 논문에서는 역전파 알고리즘의 기울기 강하 기법(gradient descent method)을 교배나 돌연변이와 같은 유전 연산자로 둠으로써 유전자 알고리즘에 지역적 미세 조정(local fine-tuning)을 위한 메카니즘을 제공해주는 새로운 형태의 GA-BP 방법을 제안한다.제안된 방법의 유용성을 보이기 위해 3-패러티 비트(3-parity bit) 문제에 실험하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3041-3063
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• 2018

Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric. This problem has recently drawn intense research interest. However, most metric-based algorithms tend to include irrelevant subgraphs in the identified community. Apart from the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of the k-hop and local distance dynamics model, which can naturally capture a community that contains the query node. The basic idea is to envision the nodes that k-hop away from the query node as an adaptive local dynamical system, where each node only interacts with its local topological structure. Relying on a proposed local distance dynamics model, the distances among nodes change over time, where the nodes sharing the same community with the query node tend to gradually move together, while other nodes stay far away from each other. Such interplay eventually leads to a steady distribution of distances, and a meaningful community is naturally found. Extensive experiments show that our community search algorithm has good performance relative to several state-of-the-art algorithms.

• Industrial Engineering and Management Systems
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• 제11권2호
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• pp.170-175
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• 2012

In this paper, a procedure using a genetic algorithm (GA) and a heuristic local search (HLS) is proposed for solving facility rearrangement problem (FRP). FRP is a decision problem for stopping/running of facilities and integration of stopped facilities to running facilities to maximize the production capacity of running facilities under the cost constraint. FRP is formulated as an integer programming model for maximizing the total production capacity under the constraint of the total facility operating cost. In the cases of 90 percent of cost constraint and more than 20 facilities, the previous solving method was not effective. To find effective alternatives, this solving procedure using a GA and a HLS is developed. Stopping/running of facilities are searched by GA. The shifting the production operation of stopped facilities into running facilities is searched by HLS, and this local search is executed for one individual in this GA procedure. The effectiveness of the proposed procedure using a GA and HLS is demonstrated by numerical experiment.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.460-465
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• 2009

This paper presents a new algorithm that includes a mechanism to avoid local solutions in a motion vector detection method that uses the steepest descent method. Two different implementations of the algorithm are demonstrated using two major search methods for tree structures, depth first search and breadth first search. Furthermore, it is shown that by avoiding local solutions, both of these implementations are able to obtain smaller prediction errors compared to conventional motion vector detection methods using the steepest descent method, and are able to perform motion vector detection within an arbitrary upper limit on the number of computations. The effects that differences in the search order have on the effectiveness of avoiding local solutions are also presented.

• 한국경영과학회지
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• 제41권1호
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• pp.41-53
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• 2016

Static repositioning is a well-known and commonly used strategy to maximize customer satisfaction in public bike-sharing systems. Repositioning is performed by trucks at night when no customers are in the system. In models that represent the static repositioning problem, the decision variables are truck routes and the number of bikes to pick up and deliver at each rental station. To simplify the problem, the decision on the number of bikes to pick up and deliver is implicitly included in the truck routes. Two relocation-based local search algorithms (1-relocate and 2-relocate) with the best-accept strategy are incorporated into a variable neighborhood search (VNS) to obtain high-quality solutions for the problem. The performances of the VNS algorithm with the effect of local search algorithms and shaking strength are evaluated with data on Tashu public bike-sharing system operating in Daejeon, Korea. Experiments show that VNS based on the sequential execution of two local search algorithms generates good, reliable solutions.