• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.129-138
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• 2006

Various techniques have been applied to solve the maximal covering problem. Tabu search is also one of them. But, existing researches were lacking of the synthetic analysis and the effort for performance improvement about neighborhood search techniques such as hill-climbing search and simulated annealing including tabu search. In this paper, I introduce the way to improve performance of neighborhood search techniques through various experiments and analyses. Basically, all neighborhood search algorithms use the k-exchange neighborhood generation method. And I analyzed how the performance of each algorithm changes according to various parameter settings. Experimental results have shown that simple hill-climbing search and simulated annealing can produce better results than any other techniques. And I confirmed that simple hill-climbing search can produce similar results as simulated annealing unlike general case.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.502-509
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• 2002

Maximal Covering 문제(MCP)란 행렬 상에서 n개의 열(column) 중 p개를 선택하여 m개의 행(row)중 최대한 많은 행을 cover하는 문제로 정의된다. 본 논문에서는 MCP를 유전 알고리즘(Genetic Algorithm)으로 해결하기 위해 문제에 적합하게 설계된 교차 연산자(crossover operator)와 비발현 유전인잔(unexpressed gene)를 가진 새로운 염색체 구조를 제시한다. 해결하고자 하는 대상 MCP의 규모가 매우 큰 경우 전통적인 임의교차(random crossover) 방법으로는 좋은 결과를 얻기가 힘들다. 따라서 본 연구에서는 그리디 교차(greedy crossover) 방법을 제시하여 문제를 해결한다. 그러나 이러한 그리디 교차를 사용하더라도 조기 수렴 등의 문제로 인해 타부 탐색 등의 이웃해 탐색 방법에 비해 그리 좋은 결과를 얻기가 힘들다. 본 논문은 이러한 조기 수렴 문제를 해결하고 다른 이웃에 탐색 방법보다 더 좋은 결과를 얻기 위해 비발현 유전인자(unexpressed gene)를 가진 염색체를 도입하여 해결함을 특징으로 한다. 비발현 유전인자는 교차 과정에서 자식 염색체의 유전인자로 전달되지 않은 정보 중 나중에라도 유용할 가능성이 보이는 정보를 보존하는 역할을 하여 조기 수렴 문제를 해결하는데 도움을 주어 보다 나은 결과를 얻을 수 있게 해준다. 대규모 MCP를 해결하는 실험에서 새로운 비발현 유전인자를 적용한 유전 알고리즘이 기존의 유전 알고리즘뿐만 아니라 다른 탐색 기법에 비해 더욱 좋은 성능을 보여줌을 확인하였다.

• Korean Management Science Review
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• v.26 no.1
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• pp.197-208
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• 2009

In this paper, we study stochastic maximal covering location problem considering floating population. Traditional maximal covering location problem assumed that number of populations at demand point is already known and fixed. In this manner, someone who try to solve real world maximal covering location problem must consider administrative population as a population at demand point. But, after observing floating population, appliance of population in steady-state is more reasonable. In this paper, we suggest revised numerical model of maximal covering location problem. We suggest heuristic methodology to solve large scale problem by using genetic algorithm.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.570-576
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• 2004

It is very difficult to efficiently solve a large-scaled maximal covering problem(MCP) by a genetic algorithm. In this paper, we present new crossover and mutation operators specially designed for genetic algorithms to solve large-scaled MCPs efficiently. We also introduce a novel genetic algorithm employing unexpressed genes. Unexpressed genes are the genes which are not expressed and thus do not affect the evaluation of the individuals. These genes play the role of reserving information susceptible to be lost by the application of genetic operations but is suspected to be potentially useful in later generations. The genetic algorithm employing unexpressed genes enjoys the advantage of being able to maintain diversity of the population and thus can search more efficiently to solve large-scaled MCPs. Experiments with large-scaled real MCP data has shown that our genetic algorithm employing unexpressed genes significantly outperforms tabu search which is one of the popularly used local neighborhood search algorithms for optimization.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.4
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• pp.139-155
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• 2006

The facility location problem of this paper is distinguished from the maximal covering location problem and the flxed-charge facility location problem. We propose the maximal profiting location problem (MPLP) that is the facility location problem maximizing profit with multi-product. We apply to the simulated annealing algorithm, the stochastic evolution algorithm and the accelerated simulated annealing algorithm to solve this problem. Through a scale-down and extension experiment, the MPLP was validated and all the three algorithm enable the near optimal solution to produce. As the computational complexity is increased, it is shown that the simulated annealing algorithm' is able to find the best solution than the other two algorithms in a relatively short computational time.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.27-35
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• 2009

Integer programming is a very effective technique for searching optimal solution of combinatorial optimization problems. However, its applicability is limited to linear models. In this paper, I propose an effective method for solving a nonlinear optimization problem by integrating the powerful search performance of integer programming and the flexibility of neighborhood search algorithms. In the first phase, integer programming is executed with subproblem which can be represented as a linear form from the given problem. In the second phase, a neighborhood search algorithm is executed with the whole problem by taking the result of the first phase as the initial solution. Through the experimental results using a nonlinear maximal covering problem, I confirmed that such a simple integration method can produce far better solutions than a neighborhood search algorithm alone. It is estimated that the success is primarily due to the powerful performance of integer programming.

• Journal of the Korean Institute of Telematics and Electronics
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• v.4 no.3
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• pp.2-8
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• 1967

A method is illustrated for minimizing the number of internal states in incompletely specified sequential networks. The starting point for minimizing technique in this paper is the set of maximal compatibility classes which covers the original flow table and the minimal covering can be obtained directly by employing three rules. The reduction techniques for prime implicant table or covering and closure table are not employed in this paper. Although the minimizing technique is applied to some specific problems, it is believed that the concepts are general in nature and can be applied to any type of incompletely specified flow tables.

• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.35-42
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• 2009

This paper proposes a new coverage algorithm for intelligent robot. Many algorithms for improving the performance of coverage have been focused on minimizing the total coverage completion time. However, if one does not have enough time to finish the whole coverage, the optimal path could be different. To tackle this problem, we propose a new coverage algorithm, which we call MaxCoverage algorithm, for covering maximal area within the deadline. The MaxCoverage algorithm decides the navigation flow by greedy algorithm for Set Covering Problem. The experimental results show that the MaxCoverage algorithm performs better than other algorithms for random deadlines.

• Journal of the Korean Society of Hazard Mitigation
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• v.2 no.1 s.4
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• pp.153-159
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• 2002

Disasters are the phenomena that we have to prevent the occurrence in order to keep the safety to human lives and properties, and if occurred, we have to minimize the economic, social, and mental costs of the occurred disasters or incidents. This research analyzes the optimal location of fire stations in terms of served population maximization in Seoul. This research introduces "the maximal covering location problem(MCLP)," one of the optimization techniques, as the primary research method. This research also applies a geographic information system into spatial distribution analyses of existing fire stations and observed fire incidents. Results from the analyses show that the existing location of fire stations and branches need to be improved. The dispatch location of fire engines should be reconsidered for rapid services of fire fighting.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.159-168
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• 2022

Local governments should try to resolve the inequality of living SOC (Social Overhead Capital) and construct spatial information on the location of living SOCs and optimal locations. This study analyzed the accessibility, equity, and optimal location of the living SOC, considering the research needs related to the living SOC. The target facility is core living facilities(a public library, a park, a culture center, and a public daycare center). The analysis area is Suwon city in Gyeonggi province, and the base year of the analysis is 2020. The study calculated accessibility per population in a microscopic neighborhood living area(200m×200m). The Gini coefficient was used to identify the regional disparity in accessibility among Dong regions. The optimal location was explored with the Maximal Covering Location Problem theory. As a result, spatial accessibility of facilities except for public daycare centers revealed a large gap between regions. Areas with excellent accessibility also showed significant variations in the facilities. The regional disparity in living SOC was the largest in culture centers, followed by parks, public daycare centers, and public libraries. The optimal locations for public libraries, parks, and culture centers are concentrated in the old downtown, while those of public daycare centers are found throughout Suwon city. The results of this study are the crucial contents of spatial planning for SOC supply in local governments. Therefore, follow-up studies will be able to refer to the analysis structure and results of the study.