• Title/Summary/Keyword: optimization problems

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A Study on Genetic Algorithms to Solve Nonlinear Optimization Problems (비선형 최적화 문제 해결을 위한 유전 알고리즘에 관한 연구)

  • 윤영수;이상용;류영근
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.19 no.40
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    • pp.15-22
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    • 1996
  • Methods to find an optimal solution that is the function of the design variables satisfying all constraints have been studied, there are still many difficulties to apply them to optimal design problems. A method to solve the above difficulties is developed by using Genetic Algorithms. but, several problems that conventional GAs are ill defined are application of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an modified GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

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A Web-based Solver for solving the Reliability Optimization Problems (신뢰도 최적화 문제에 대한 웹기반의 Solver 개발)

  • 김재환
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.8 no.1
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    • pp.127-137
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    • 2002
  • This paper deals with developing a Web-based Solver NRO(Network Reliability Optimizer) for solving three classes of reliability redundancy optimization problems which are generated in series systems. parallel systems and complex systems. Inputs of NRO consisted in four parts. that is, user authentication. system selection. input data and confirmation. After processing of inputs through internet, NRO provides conveniently the optimal solutions for the given problems on the Web-site. To alleviate the risks of being trapped in a local optimum, HH(Hybrid-Heuristic) algorithm is incorporated in NRO for solving the given three classes of problems, and moderately combined GA(Genetic Algorithm) with the modified SA(Simulated Annealing) algorithm.

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Generating Mechanisms of Initial and Candidate Solutions in Simulated Annealing for Packet Communication Network Design Problems (패킷 통신 네트워크 설계를 위한 시뮬레이티드 애닐링 방법에서 초기해와 후보해 생성방법)

  • Yim Dong-Soon;Woo Hoon-Shik
    • Journal of the Korean Operations Research and Management Science Society
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    • v.29 no.3
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    • pp.145-155
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    • 2004
  • The design of a communication network has long been a challenging optimization problem. Since the optimal design of a network topology is a well known as a NP-complete problem, many researches have been conducted to obtain near optimal solutions in polynomial time instead of exact optimal solutions. All of these researches suggested diverse heuristic algorithms that can be applied to network design problems. Among these algorithms, a simulated annealing algorithm has been proved to guarantee a good solution for many NP-complete problems. in applying the simulated annealing algorithms to network design problems, generating mechanisms for initial solutions and candidate solutions play an important role in terms of goodness of a solution and efficiency. This study aims at analyzing these mechanisms through experiments, and then suggesting reliable mechanisms.

Genetic algorithms for optimization : a case study of machine-part group formation problems (기계-부품군 형성문제의 사례를 통한 유전 알고리즘의 최적화 문제에의 응용)

  • 한용호;류광렬
    • Korean Management Science Review
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    • v.12 no.2
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    • pp.105-127
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    • 1995
  • This paper solves different machine-part group formation (MPGF) problems using genetic algorithms to demonstrate that it can be a new robust alternative to the conventional heuristic approaches for optimization problems. We first give an overview of genetic algorithms: Its principle, various considerations required for its implementation, and the method for setting up parameter values are explained. Then, we describe the MPGF problem which are critical to the successful operation of cellular manufacturing or flexible manufacturing systems. We concentrate on three models of the MPGF problems whose forms of the objective function and/or constraints are quite different from each other. Finally, numerical examples of each of the models descibed above are solved by using genetic algorithms. The result shows that the solutions derived by genetic algorithms are comparable to those obtained through problem-specific heuristic methods.

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Development of an Efficient Line Search Method by Using the Sequential Polynomial Approximation (순차적 다항식 근사화를 적용한 효율적 선탐색기법의 개발)

  • 김민수;최동훈
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.19 no.2
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    • pp.433-442
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    • 1995
  • For the line search of a multi-variable optimization, an efficient algorithm is presented. The algorithm sequentially employs several polynomial approximations such as 2-point quadratic interpolation, 3-point cubic interpolation/extrapolation and 4-point cubic interpolation/extrapolation. The order of polynomial function is automatically increased for improving the accuracy of approximation. The method of approximation (interpolation or extrapolation) is automatically switched by checking the slope information of the sample points. Also, for selecting the initial step length along the descent vector, a new approach is presented. The performance of the proposed method is examined by solving typical test problems such as mathematical problems, mechanical design problems and dynamic response problems.

Differential Evolution Algorithm for Job Shop Scheduling Problem

  • Wisittipanich, Warisa;Kachitvichyanukul, Voratas
    • Industrial Engineering and Management Systems
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    • v.10 no.3
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    • pp.203-208
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    • 2011
  • Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.

Harmony Search Algorithm-Based Approach For Discrete Size Optimization of Truss Structures

  • Lee Kang-Seok;Kim Jeong-Hee;Choi Chang-Sik;Lee Li-Hyung
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2005.04a
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    • pp.351-358
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    • 2005
  • Many methods have been developed and are in use for structural size optimization problems, In which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary In this paper, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through a standard truss example. The numerical results reveal that the proposed method is a powerful search and design optimization tool for structures with discrete-sized members, and may yield better solutions than those obtained using current method.

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Shape Design Optimization of Ship Structures Considering Thermal Deformation and Target Shape (열 변형과 목적형상을 고려한 선체구조의 형상 최적설계)

  • Park, Sung-Ho;Choi, Jae-Yeon;Kim, Min-Geun;Cho, Seon-Ho
    • Journal of the Society of Naval Architects of Korea
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    • v.47 no.3
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    • pp.430-437
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    • 2010
  • In this paper, we develop a shape design optimization method for thermo-elastoplasticity problems that is applicable to the welding or thermal deformation problems of ship structures. Shell elements and a programming language APDL in a commercial finite element analysis code, ANSYS, are employed in the shape optimization. The point of developed method is to determine the design parameters such that the deformed shape after welding fits very well to a desired design. The geometric parameters of surfaces are selected as the design parameters. The modified method of feasible direction (MMFD) and finite difference sensitivity are used for the optimization algorithm. Two numerical examples demonstrate that the developed shape design method is applicable to existing hull structures and effective for the structural design of ships.

A Study on Strengthened Genetic Algorithm for Multi-Modal and Multiobjective Optimization (강화된 유전 알고리듬을 이용한 다극 및 다목적 최적화에 관한 연구)

  • Lee Won-Bo;Park Seong-Jun;Yoon En-Sup
    • Journal of the Korean Institute of Gas
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    • v.1 no.1
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    • pp.33-40
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    • 1997
  • An optimization system, APROGA II using genetic algorithm, was developed to solve multi-modal and multiobjective problems. To begin with, Multi-Niche Crowding(MNC) algorithm was used for multi-modal optimization problem. Secondly, a new algorithm was suggested for multiobjective optimization problem. Pareto dominance tournaments and Sharing on the non-dominated frontier was applied to it to obtain multiple objectives. APROGA II uses these two algorithms and the system has three search engines(previous APROGA search engine, multi-modal search engine and multiobjective search engine). Besides, this system can handle binary and discrete variables. And the validity of APROGA II was proved by solving several test functions and case study problems successfully.

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A Holistic Approach to Optimizing the Lifetime of IEEE 802.15.4/ZigBee Networks with a Deterministic Guarantee of Real-Time Flows

  • Kim, Kang-Wook;Park, Myung-Gon;Han, Junghee;Lee, Chang-Gun
    • Journal of Computing Science and Engineering
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    • v.9 no.2
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    • pp.83-97
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    • 2015
  • IEEE 802.15.4 is a global standard designed for emerging applications in low-rate wireless personal area networks (LR-WPANs). The standard provides beneficial features, such as a beacon-enabled mode and guaranteed time slots for realtime data delivery. However, how to optimally operate those features is still an open issue. For the optimal operation of the features, this paper proposes a holistic optimization method that jointly optimizes three cross-related problems: cluster-tree construction, nodes' power configuration, and duty-cycle scheduling. Our holistic optimization method provides a solution for those problems so that all the real-time packets can be delivered within their deadlines in the most energy-efficient way. Our simulation study shows that compared to existing methods, our holistic optimization can guarantee the on-time delivery of all real-time packets while significantly saving energy, consequently, significantly increasing the lifetime of the network. Furthermore, we show that our holistic optimization can be extended to take advantage of the spatial reuse of a radio frequency resource among long distance nodes and, hence, significantly increase the entire network capacity.