• 한국컴퓨터정보학회논문지
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• 제16권3호
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• pp.203-210
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• 2011

개미 집단 시스템은 조합 최적화 문제를 해결하기 위한 메타 휴리스틱 탐색 방법으로, 그리디 탐색뿐만 아니라 긍정적 피드백을 사용한 모집단에 근거한 접근법으로 순회 판매원 문제를 풀기 위해 처음으로 제안되었다. 본 논문에서는 이전 전역 최적 경로와 현재 전역 최적 경로의 중복 간선을 고려한 탐색 방법을 제안하였다. 이 방법은 이전전역 최적 경로와 현재 전역 최적 경로에서의 중복 간선은 최적 경로로 구성될 가능성이 높다고 판단하고, 해당 중복 간선에 대해 페로몬을 강화시켜 최적 경로를 구성할 확률을 높이게 하였다. 그리고, 실험을 통해 ACS-3-opt 알고리즘, ACS-Subpath 알고리즘, ACS-Iter 알고리즘에 비해 최적 경로 탐색 및 평균 최적 경로 탐색의 성능이 우수함을 보여 주었다.

• 한국산업정보학회논문지
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• 제28권5호
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• pp.67-75
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• 2023

본 논문에서는 도시 내 교통 혼잡 문제를 해결하기 위한 새로운 혁신 기술 중 하나인 에어 택시 운영에 관한 연구를 다룬다. 성공적인 기술 도입과 합리적인 운영을 위해 초기에 고려해야 할 문제 중 하나인 수직이착륙장(vertiport) 위치 결정 문제를 다룬다. 교통수단 이용에 따른 비용과 이동시간을 고려하여 각 경로에서의 교통수단 예측 수요 확률을 이산 선택 모형을 활용하여 구하고, 이를 반영하여 에어 택시 이용률의 최대화를 목적으로 하는 수리적 모형을 제안한다. 본 수리적 모형은 NP-난해(NP-hard) 문제로, 위치 결정 문제를 해결하기 위한 효과적이면서 효율적인 문제 해결방법론이 필요하다. 단순히 최적화 모형을 제안한 기존 연구와 달리 본 연구에서는 교차-엔트로피 알고리즘(cross-entropy algorithm)을 활용한 문제 해결 방법론을 제안하고, 수치 실험을 통해 알고리즘의 효과성과 효율성을 확인한다. 문제 해결 방법론의 학술적 우수성 외에도, 실제 데이터 및 에어 택시 활용 계획을 고려한 의사결정의 제시는 실무적인 활용 가능성을 높일 수 있음을 시사한다.

• 전기학회논문지
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• 제58권6호
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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.

• 한국콘텐츠학회논문지
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• 제5권3호
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• pp.193-199
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• 2005

휴리스틱 알고리즘 연구에 있어서 중요한 분야 중 하나가 강화와 다양화의 조화를 맞추는 문제이다. 개미 집단 시스템은 최근에 제안된 조합 최적화문제를 해결하기 위한 메타 휴리스틱 기법으로, 그리디 탐색과 긍정적 보상에 의한 접근법으로 순회 판매원 문제를 풀기 위해 처음으로 제안되었다. 본 논문에서는 기존 개미집단 시스템의 성능을 향상시키기 위해 강화 전략과 다양화 전략으로 나누어진 엘리트 전략을 통해 집단간 긍정적 부정적 상호작용을 수행하는 다중 집단 개미 모델을 제안한다. 그리고, 이 제안된 엘리트 전략에 의한 다중 집단 상호작용 개미 모델을 순회판매원문제에 적용해 보고 그 성능에 대해 기존 개미집단 시스템과 비교한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4347-4366
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• 2015

Distinguished with traditional strategies which offload an application's computation to a single server, parallel computation offloading can promote the performance by simultaneously delivering the computation to multiple computing resources around the mobile terminal. However, due to the variability of communication and computation environments, static application component multi-partitioning algorithms are difficult to maintain the optimality of their solutions in time-varying scenarios, whereas, over-frequent algorithm executions triggered by changes of environments may bring excessive algorithm costs. To this end, an adaptive application component mapping algorithm for parallel computation offloading in variable environments is proposed in this paper, which aims at minimizing computation costs and inter-resource communication costs. It can provide the terminal a suitable solution for the current environment with a low incremental algorithm cost. We represent the application component multi-partitioning problem as a graph mapping model, then convert it into a pathfinding problem. A genetic algorithm enhanced by an elite-based immigrants mechanism is designed to obtain the solution adaptively, which can dynamically adjust the precision of the solution and boost the searching speed as transmission and processing speeds change. Simulation results demonstrate that our algorithm can promote the performance efficiently, and it is superior to the traditional approaches under variable environments to a large extent.

• 한국정보시스템학회지:정보시스템연구
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• 제25권1호
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.

• 한국멀티미디어학회논문지
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• 제7권6호
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• pp.841-850
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• 2004

This paper proposes a GA and GDM-based method for removing unnecessary rules and generating relevant rules from the fuzzy rules corresponding to several fuzzy partitions. The aim of proposed method is to find a minimum set of fuzzy rules that can correctly classify all the training patterns. When the fine fuzzy partition is used with conventional methods, the number of fuzzy rules has been enormous and the performance of fuzzy inference system became low. This paper presents the application of GA as a means of finding optimal solutions over fuzzy partitions. In each rule, the antecedent part is made up the membership functions of a fuzzy set, and the consequent part is made up of a real number. The membership functions and the number of fuzzy inference rules are tuned by means of the GA, while the real numbers in the consequent parts of the rules are tuned by means of the gradient descent method. It is shown that the proposed method has improved than the performance of conventional method in formulating and solving a combinatorial optimization problem that has two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy rules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2240-2254
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• 2021

We investigate the channel allocation problem in an ultra-dense device-to-device (D2D) enabled cellular network in underlaying mode where multiple D2D users are forced to share the same channel. Two kinds of low complexity solutions, which just require partial channel state information (CSI) exchange, are devised to resolve the combinatorial optimization problem with the quality of service (QoS) guaranteeing. We begin by sorting the cellular users equipment (CUEs) links in sequence in a matric of interference tolerance for ensuring the SINR requirement. Moreover, the interference quota of CUEs is regarded as one kind of communication resource. Multiple D2D candidates compete for the interference quota to establish spectrum sharing links. Then base station calculates the occupation of interference quota by D2D users with partial CSI such as the interference channel gain of D2D users and the channel gain of D2D themselves, and carries out the channel allocation by setting different access priorities distribution. In this paper, we proposed two novel fast matching algorithms utilize partial information rather than global CSI exchanging, which reduce the computation complexity. Numerical results reveal that, our proposed algorithms achieve outstanding performance than the contrast algorithms including Hungarian algorithm in terms of throughput, fairness and access rate. Specifically, the performance of our proposed channel allocation algorithm is more superior in ultra-dense D2D scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• Industrial Engineering and Management Systems
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• 제8권3호
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• pp.194-200
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• 2009

This study provides a heuristic algorithm to solve the locating problem of repair service centers (RSCs). To enhance the customer service level with more satisfaction and quicker responsiveness, the locating problem of RSCs has become one of the important issues in reverse supply chain management. This problem is formulated as a zero-one mixed integer programming in which an exiting distributor will be considered to be an un-capacitated repair service center for the objective of cost-minimizing. Since logistical costs are highly interrelated with the multinational location of distributors and RSCs, the fixed cost for setting a repair service center, variable cost, transportation cost, and exchange rates are considered in this study. Recognizing the selection of un-capacitated RSCs' locations is a combinatorial optimization problem and is a zero-one mixed integer programming with NP-hard complexity, we provide a heuristic algorithm named as incremental cost matrix procedure (ICMP) to simplify the solving procedure. By using the concise and structural cost matrix, ICMP can efficiently screen the potential location with cost advantage and effectively decide which distributor should be a RSC. Results obtained from the numerical experiments conducted in small scale problem have shown the fact that ICMP is an effective and efficient heuristic algorithm for solving the RSCs locating problem. In the future, using the extended ICMP to solve problems with larger industrial scale or problems with congestion effects caused by the variation of customer demand and the restriction of the RSC capacity is worth a further investigation.