• Title/Summary/Keyword: Knapsack Problem

Search Result 80, Processing Time 0.258 seconds

GPU-Based Acceleration of Quantum-Inspired Evolutionary Algorithm (GPU를 이용한 Quantum-Inspired Evolutionary Algorithm 가속)

  • Ryoo, Ji-Hyun;Park, Han-Min;Choi, Ki-Young
    • Journal of the Institute of Electronics Engineers of Korea SD
    • /
    • v.49 no.8
    • /
    • pp.1-9
    • /
    • 2012
  • Quantum-Inspired Evolutionary Algorithm(QEA) contains sufficient data-level parallelism to be naturally accelerated on GPUs. For an efficient reduction of execution time, however, careful task-mapping should be done to properly reflect the characteristics of CPU and GPU. Furthermore, when deciding which part of the application should run on GPU, we need to consider the data transfer between CPU and GPU memory spaces as well as the data-level parallelism. In addition, the usage of zero-copy host memory, proper choice of the execution configuration, and thread organization considering memory coalescing is important to further reduce the execution time. With all these techniques, we could run QEA 3.69 times faster on average in comparison with the multi-threading CPU for the case of 0-1 knapsack problem with 30,000 items.

A Priority-Based Bandwidth Management Method in Public Safety Networks (재난 안전 통신망에서 우선순위를 고려한 대역폭 관리 방법)

  • Lee, Sang-Hoon;Kim, Hyun-Woo;Yoon, Hyun-Goo;Choi, Yong-Hoon
    • The Journal of The Korea Institute of Intelligent Transport Systems
    • /
    • v.15 no.2
    • /
    • pp.102-110
    • /
    • 2016
  • After Sewol ferry disaster occurred in April 2014, Korean government began investing to deploy LTE-based public safety network until the year of 2017. In order to reduce the operating and capital costs, resource sharing scheme among public safety network and commercial LTE networks is considered as one of the viable approaches. This thesis proposes a method of allocating bandwidth of public safety network based on various priorities required for disaster scenarios and stages in a resource sharing environment. In order to obtain the highest efficiency, we formulate the bandwidth allocation problem as a Fractional Knapsack Problem. Greedy algorithm was applied to solve the problem. For performance evaluation, we created several disaster scenarios and set suitable parameters for each scenario based on a disaster manual. The proposed method is compared with two typical methods, which are Class-based bandwidth allocation and Uniform bandwidth allocation. The results showed that the better performance in terms of the sum of the values and the amount of lost bytes.

Efficient Satellite Mission Scheduling Problem Using Particle Swarm Optimization (입자 군집 최적화 방법론을 이용한 효율적 위성임무 일정 수립에 관한 연구)

  • Lee, Youngin;Lee, Kangwhan;Seo, Inwoo;Ko, Sung-Seok
    • Journal of the Society of Korea Industrial and Systems Engineering
    • /
    • v.39 no.1
    • /
    • pp.56-63
    • /
    • 2016
  • We consider a satellite mission scheduling problem, which is a promising problem in recent satellite industry. This problem has various considerations such as customer importance, due date, limited capacity of energy and memory, distance of the location of each mission, etc. Also we consider the objective of each satellite such as general purpose satellite, strategic mission and commercial satellite. And this problem can be modelled as a general knapsack problem, which is famous NP-hard problem, if the objective is defined as to maximize the total mission score performed. To solve this kind of problem, heuristic algorithm such as taboo and genetic algorithm are applied and their performance are acceptable in some extent. To propose more efficient algorithm than previous research, we applied a particle swarm optimization algorithm, which is the most promising method in optimization problem recently in this research. Owing to limitation of current study in obtaining real information and several assumptions, we generated 200 satellite missions with required information for each mission. Based on generated information, we compared the results by our approach algorithm with those of CPLEX. This comparison shows that our proposed approach give us almost accurate results as just less than 3% error rate, and computation time is just a little to be applied to real problem. Also this algorithm has enough scalability by innate characteristic of PSO. We also applied it to mission scheduling problem of various class of satellite. The results are quite reasonable enough to conclude that our proposed algorithm may work in satellite mission scheduling problem.

A Joint Allocation and Path Selection Scheme for Downlink Transmission in LTE-Advanced Relay System with Cooperative Relays (협력 통신을 이용한 LTE-Advanced 릴레이 시스템을 위한 하향링크 통합 자원할당 및 경로선택 기법)

  • Lee, Hyuk Joon;Um, Tae Hyun
    • The Journal of The Korea Institute of Intelligent Transport Systems
    • /
    • v.17 no.6
    • /
    • pp.211-223
    • /
    • 2018
  • Mobile relay systems have been adopted by $4^{th}$ generation mobile systems as an alternative method to extend cell coverage as well as to enhance the system throughput at cell-edges. In order to achieve such performance gains, the mobile relay systems require path selection and resource allocation schemes that are specifically designed for these systems which make use of additional radio resources not needed in single-hop systems. This paper proposes an integrated path selection and resource allocation scheme for LTE-Advanced relay systems using collaborative communication. We first define the problem of maximizing the downlink throughput of LTE-Advanced relay systems using collaborative communication and transform it into a multi-dimensional multi-choice backpacking problem. The proposed Lagrange multiplier-based heuristic algorithm is then applied to derive the approximate solution to the maximization problem. It is shown through simulations that the approximate solution obtained by the proposed scheme can achieve a near-optimal performance.

Genetic Algorithm Applying Modified Mutation Operator Based on Hamming Distance for Solving Multi-dimensional Knapsack Problem (개체간 해밍 거리 기반의 변이연산을 적용한 유전알고리즘을 이용한 다차원 배낭 문제 탐색)

  • Jeong, Jae-Hun;Lee, Jong-Hyun;Ahn, Chang-Wook
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • /
    • pp.1728-1731
    • /
    • 2012
  • 본 논문에서는 부모 개체의 해밍 거리에 기반하여 선택적 변이연산을 적용한 유전알고리즘을 제안한다. 유전자 형이 매우 유사한 개체들 간의 유전연산은 알고리즘의 탐색성능을 저하시키고 조기 수렴의 가능성을 증가시킨다. 본 논문에서는 이러한 현상을 극복하기 위하여, 교차연산 시 선택된 두 부모 개체간의 해밍 거리에 따라 그 값이 낮으면 교차연산 후 생성된 두 자식 개체 중 한쪽에게 높은 변이확률을 적용하고 다른 한쪽 자식은 부모와 비슷한 유전자 형으로 탐색을 계속하게 하여 조기 수렴을 방지하면서 해집단의 다양성 유지 기능을 향상 시켰다. 제안한 유전 알고리즘을 다차원 배낭 문제에 적용한 결과, 같은 조건에서 단순 유전 알고리즘(SGA) 보다 향상된 탐색 성능을 보여주었다.

  • PDF

An Analysis of the Relationship between Problem Characteristics and Algorithm Performance : A Case Study on 0-1 Knapsack Problems (문제 특성과 알고리듬 수행 능력 간 관계에 관한 분석 : 0-1 Knapsack 문제에 관한 사례 연구)

  • Yang Jae-Hwan;Kim Hyun-Soo
    • Journal of the Korean Operations Research and Management Science Society
    • /
    • v.31 no.1
    • /
    • pp.55-71
    • /
    • 2006
  • We perform a computational study on 0-1 knapsack problems generated under explicit correlation induction. A total of 2000 100-variable test problems are solved. We use two solution methods: (1) a well known heuristic and (2) a representative branch and bound type algorithm. Two different performance measures are considered: (1) the number of nodes needed to find an optimal solution and (2) the relative error of the heuristic solution. We also examine the effect of different joint probability mass functions (pmfs) for the coefficient values on the performance of the solution procedure.

Sensor deployment and movement algorithm for improvement sensing efficiency in the Underwater Wireless Sensor Networks (수중 센서 네트워크에서 향상된 인식 효율성을 위한 센서의 배치 및 이동 알고리즘)

  • Lee, Jong-Geun;Park, Hyun-Hoon;Park, Jin-Ho;Kim, Sung-Un
    • Proceedings of the IEEK Conference
    • /
    • /
    • pp.63-64
    • /
    • 2007
  • The Underwater Wireless Sensor Networks (UWSN) consists of sensor nodes equipped with limited sensing coverages, energy resources and communication capacity. Hence, the deployment and movement algorithm is a key issue that needs to be organized in order to improve the sensing efficiency of the networks. In this paper, we use a Queen problem and Knapsack problem to prevent the reiteration phenomenon of sensors, to guarantee improvement sensing coverage and efficiency in the 3D UWSN.

  • PDF

A New Upper Bound for Two-Dimensional Guillotine Cutting Problem (2차원 길로틴 절단문제를 위한 새로운 상한)

  • 윤기섭;지영근;강맹규
    • Journal of the Society of Korea Industrial and Systems Engineering
    • /
    • v.24 no.62
    • /
    • pp.21-32
    • /
    • 2001
  • The two-dimensional guillotine cutting problem is to maximize sum of piece profits that cut from one stock rectangle and widely applied in the industry. The branch-and-bound method for this problem uses complementarily several upper bounds(the Gilmore and Gomoryp[8]'s two-dimensional knapsack function and the Hifi and Zissimopoulos[10]'s method using one-dimensional knapsack problem, etc) to reduce the number of searched nodes. These upper bounds has a shortcoming that does not consider the bound and layout of pieces simultaneously. In this paper, we propose an efficient upper bound which can complement the shortcoming of existing upper bounds. The proposed upper bound needs less memory spaces and computing time. Computational results show that the proposed upper bound significantly contribute to reduce the computational amount of time and number of searched nodes in tree.

  • PDF

A GA-based Heuristic for the Interrelated Container Selection Loading Problems

  • Techanitisawad, Anulark;Tangwiwatwong, Paisitt
    • Industrial Engineering and Management Systems
    • /
    • v.3 no.1
    • /
    • pp.22-37
    • /
    • 2004
  • An integrated heuristic approach based on genetic algorithms (GAs) is proposed for solving the container selection and loading problems. The GA for container selection solves a two-dimensional knapsack problem, determining a set of containers to minimize the transportation or shipment cost. The GA for container loading solves for the weighted coefficients in the evaluation functions that are applied in selecting loading positions and boxes to be loaded, so that the volume utilization is maximized. Several loading constraints such as box orientation, stack priority, stack stability, and container stability are also incorporated into the algorithm. In general, our computational results based on randomly generated data and problems from the literature suggest that the proposed heuristic provides a good solution in a reasonable amount of computational time.

Constructing Container Shipping Networks with Empty Container Repositioning among Calling Ports - a Genetic Algorithm Approach

  • Shintani, Koichi;Imai, Akio;Nishmura, Etsuko;Papadimitriou, Stratos
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
    • /
    • v.2
    • /
    • pp.157-164
    • /
    • 2006
  • This paper addresses the design of container liner shipping service networks by explicitly taking into account empty container repositioning and container fleet size. Two key and interrelated issues of deployments of ships and containers are usually treated separately by most existing studies on shipping network design. In this paper, both issues are considered simultaneously. The problem is formulated as a two-stage problem: the upper-problem being formulated as a Knapsack problem and the lower-problem as a Flow problem. A genetic algorithm based heuristic is developed for the problem. Through a number of numerical experiments that were conducted it was shown that the problem considering empty container repositioning provides a more insightful solution than the one without.

  • PDF