• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.157-164
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• 2023

This paper suggests O(n²) polynomial time heuristic algorithm for corporate tax structure optimization problem that has been classified as NP-complete problem. The proposed algorithm constructs tax tree levels that the target holding company is located at root node of Level 1, and the tax code categories(T_e) 1,4,3,2 are located in each level 2,3,4,5 sequentially. To find the maximum tax-relief path from source(S) to target(T), firstly we connect the minimum witholding tax rate _minr_w(u, v) arc of node u point of view for transfer the profit from u to v node. As a result we construct the spanning tree from all of the source nodes to a target node, and find the initial feasible solution. Nextly, we find the alternate path with minimum foreign tax rate _minr_fi(u, v) of v point of view. Finally we choose the minimum tax-relief path from of this two paths. The proposed heuristic algorithm performs better optimal results than linear programming and Tabu search method that is a kind of metaheuristic method.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.4
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• pp.13-16
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• 1984

This paper proposes a new layout algorithm in order to minimize chip area in one dimensional MOS - LSI composed of basic cells, such as NAND or NOR gates. The virtval gates are constructed, which represent I/O of signal lines at the left-most and at the right-most side of the MCS gate array. With this, a heuristic algorithm is realized that can minimize the number of straight connectors passing through each gate, and as the result, minimize the horizontal tracks necessary to route. The usefulness of the algorithm proposed is shown by the execution of the experimental program on practical logic circuits.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.279-282
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• 2001

병렬프로그램을 멀티프로세서로 스케줄링하는 문제의 해를 구하기 위하여 본 논문에서는 확장된 유전자 알고리즘을 적용한다. 확장된 유전자알고리즘인 MSEGA는 각 노드의 선행관계에 관한 휴리스틱한 정보와 간단한 일차원 배열구조가 통합된 염색체 코딩방법과 염색체 구성인자 중 우성 유전인자의 형질을 다음세대로 존속시키는 교배연산자와 프로세서 효율성이 고려된 평가 함수등으로 순서제약이 있는 병렬프로그램 스케줄링 문제 및 FFT(Fast Fourier Transform)형태의 데이터 흐름도상에서 관련 연구 중 Hou의 유전자 알고리즘과 BEA(binary-exchange algorithm)에 의한 스케줄링 결과보다 전체실행시간에 있어 HSEGA에 의한 스케줄링이 더 우수함을 보였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.236-244
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• 2012

The minimum energy broadcast problem is for all deployed nodes to minimize a total transmission energy for performing a broadcast operation in wireless networks. In this paper, we propose a Tabu search algorithm to solve efficiently the minimum energy broadcast problem on the basis of meta-heuristic approach in wireless sensor networks. In order to make a search more efficient, we propose a novel neighborhood generating method and a repair function of the proposed algorithm. We compare the performance of the proposed algorithm with other existing algorithms through some experiments in terms of the total transmission energy of nodes and algorithm computation time. Experimental results show that the proposed algorithm is efficient for the minimum energy broadcast problem in wireless sensor networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1715-1724
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• 2013

The maximum lifetime data aggregation problem is to maximize the network lifetime as minimizing the transmission energy of all deployed nodes in wireless sensor networks. In this paper, we propose a simulated annealing algorithm to solve efficiently the maximum lifetime data aggregation problem on the basis of meta-heuristic approach in wireless sensor networks. In order to make a search more efficient, we propose a novel neighborhood generating method and a repair function of the proposed algorithm. We compare the performance of the proposed algorithm with other existing algorithms through some experiments in terms of the network lifetime and algorithm computation time. Experimental results show that the proposed algorithm is efficient for the maximum lifetime data aggregation problem in wireless sensor networks.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.79-87
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• 2023

In this paper, we propose a novel genetic algorithm to reduce the overall span time by optimizing the skid insertion sequence in the shipyard subassembly process. We represented a solution by a permutation of a set of skid ids and applied genetic operators suitable for such a representation. In addition, we combined the genetic algorithm and the existing heuristic algorithm called UniDev which is properly modified to improve the search performance. In particular, the slow skid search part in UniDev was changed to a greedy algorithm. Through extensive large-scaled simulations, it was observed that the span time of our method was stably minimized compared to Multi-Start search and a genetic algorithm combined with UniDev.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.91-99
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• 2014

TThis paper suggests an heuristic polynomial time algorithm to solve the optimal solution for QAP (quadratic assignment problem). While Hungarian algorithm is most commonly used for a linear assignment, there is no polynomial time algorithm for the QAP. The proposed algorithm derives a grid type layout among unit distances of a distance matrix. And, we apply max-flow/min-distance approach to assign this grid type layout in such a descending order way that the largest flow is matched to the smallest unit distance from flow matrix. Evidences from implementation results of the proposed algorithm on various numerical grid type QAP examples show that a solution to the QAP could be obtained by a polynomial algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.169-175
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• 2021

The lot sizing problem(LSP) is a hard problem that classified as non-deterministic(NP)-complete because of the polynomial-time optimal solution algorithm is unknown yet. The well-known W-W algorithm can be obtain the solution within polynomial-time, but this algorithm is a very complex, therefore the heuristic approximated S-M algorithm is suggested. This paper suggests O(n) linear-time complexity algorithm that can be find not the approximated but optimal solution. This algorithm determines the lot size Xt∗ in period t to the sum of the demands of interval [t,t+k], the period t+k is determined by the holding cost will not exceed setup cost of t+k period. As a result of various experimental data, this algorithm finds the optimal solution about whole data.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.636-643
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• 2020

The differential evolution algorithm is one of the meta-heuristic techniques developed to solve the real optimization problem, which is a continuous problem space. In this study, in order to use the differential evolution algorithm to solve the traveling salesman problem, which is a discontinuous problem space, a random key representation method is applied to the differential evolution algorithm. The differential evolution algorithm searches for a real space and uses the order of the indexes of the solutions sorted in ascending order as the order of city visits to find the fitness. As a result of experimentation by applying it to the benchmark traveling salesman problems which are provided in TSPLIB, it was confirmed that the proposed differential evolution algorithm based on the random key representation method has the potential to solve the traveling salesman problems.