• Journal of Smart Tourism
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• v.3 no.3
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• pp.29-40
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• 2023

This research aims to understand the role of online reviews in the local search context by examining the effects of reviews on the representation of tourism businesses on local search platforms (LSPs). By simulating tourists' local searches for restaurants on three LSPs, namely Google, Bing, and Yelp, this study examines how different ranking results are generated across the platforms and how online reviews contribute to the differences. The findings suggest that online reviews are incorporated into LSPs as ranking factors and, thus, affect tourists' decision-making by influencing the information search results in the local search context. As one of the earliest studies on local search, this study discusses how the existing knowledge about the role of online reviews in tourists' decision-making needs to be reevaluated in mobile and more dynamic environments, and offers practical implications for tourism businesses' search engine marketing.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.183-186
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• 2003

Hybrid genetic algorithms (HGAs) have been studied as various ways. These HGAs usually use both the global search property of genetic algorithm (GA) and the local search one of local search techniques. One of the general types, when constructing HGAs, is to incorporate a local search technique into GA loop, and then the local search technique is repeated as many iteration number as the loop. This paper proposes a new HGA with a conditional local search technique (c-HGA) that does not be repeated as many iteration number as GA loop. For effectiveness of the proposed c-HGA, a conventional HGA and GA are also suggested, and then these algorithms are compared with each other in numerical examples,

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.356-364
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• 2000

Block based fast motion estimation algorithm use the fixed search pattern to reduce the search point, and are based on the assumption that the error in the mean absolute error space monotonically decreases to the global minimum. Therefore, in case of many local minima in a search region we are likely to find local minima instead of the global minimum and highly rely on the initial search points. This situation is evident in the motion boundary. In this paper we define the candidate regions within the search region using the motion information of the neighbor blocks and we propose the multiple local search method (MLSM) which search for the solution throughout the candidate regions to reduce the possibilities of isolation to the local minima. In the MLSM we mark the candidate region in the search point map and we avoid to search the candidate regions already visited to reduce the calculation. In the simulation results the proposed method shows more excellent results than that of other gradient based method especially in the search of motion boundary. Especially, in PSNR the proposed method obtains similar estimate accuracy with the significant reduction of search points to that of full search.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.39-47
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• 2010

Constraint satisfaction optimization problem is a kind of optimization problem involving cost minimization as well as complex constraints. Local search and constraint programming respectively have been used for solving such problems. In this paper, I propose a method to integrate local search and constraint programming to improve search performance. Basically, local search is used to solve the given problem. However, it is very difficult to find a feasible neighbor satisfying all the constraints when we use only local search. Therefore, I introduced constraint programming as a tool for neighbor generation. Through the experimental results using weighted N-Queens problems, I confirmed that the proposed method can significantly improve search performance.

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.48-53
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• 2002

This paper Proposes an iterative Local Search (ILS) algorithm for Rural Postman Problems (RPPs). LS searches neighbors from an initial solution in solution space and obtains a nearoptimal solution which can be a local-minima. As an extension of LS, the ILS algorithm is a method that uses various initial solutions for LS. Hence. ILS can overcome the defect of LS. This paper proposes LS and ILS methods for 18 RPPs and analyzes the results of LS and ILS. In the simulation results, the ILS method obtained the better results than the LS method.

• IE interfaces
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• v.18 no.4
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.488-491
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• 1995

The paper presents a new global maximum search method for multimodal unknown functions of two variables. The search method is composed of two stages and sequentially samples the candidate point in a subdomain selected using a priority function in each stage. The search domain is auto-similarly divided into triangular subdomains, or cells, during the search process. A measure of accuracy of local maximum search is introduced to check if a local search has converged to a specified accuracy or the maximum of a local peak cannot be the global maximum. A criterion for switching from the first to the second stage, is proposed using a ratio of the observed peak width to the largest cell in the domain. By numerical simulations, the required number of trials is evaluated for some function models with different peak parameters, and the switching criterion is optimally determined. The results show that the proposed method obtains global maximum points with certainty and saves largely computation time even for functions with extremely steep peaks.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.362-368
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• 2005

This paper is related to the fast block matching algorithm, especially a method for expanding the search pattern using the second local matching point in the adaptive hexagonal search. To reduce the local minima problem in fast motion estimation, the proposed method expands the search pattern by adding new searching points selected by using the second local matching point to conventional search pattern formed by the first local matching point in the adaptive hexagonal search. According to estimating the motion vector by applying block matching algorithm based on hexagonal search to the expanded search pattern, the proposed method can effectively carry out fast motion estimation to improve the performance in terms of compensated image quality.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.1
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.10
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• pp.565-572
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• 2004

This paper proposes an expert system which can enhance the accuracy of real-time bus reconfiguration strategy by adopting local minimum tree search method and minimize the spreading effect of the fault by considering totally the operating condition when a main transformer fault occurs in the automated substation. The local minimum tree search method to expand the best-first search method. This method has an advantage which can improve the performance of solution within the limits of the real-time condition. The inference strategy proposed expert system consists of two stages. The first stage determines the switching candidate set by searching possible switching candidates starting from the main transformer or busbar related to the event. And, second stage determines the rational real-time bus reconfiguration strategy based on heuristic rules for the obtained switching candidate set. Also, this paper studies the generalized distribution substation modelling using graph theory and a substation database is designed based on the study result. The inference engine of the expert system and the substation database is implemented in MFC function of Visual C++. Finally, the performance and effectiveness of the proposed expert system is verified by comparing the best-first search solution and local minimum tree search solution based on diversity event simulations for typical distribution substation.