• Title, Summary, Keyword: Local optimization

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Water Flowing and Shaking Optimization

  • Jung, Sung-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.12 no.2
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    • pp.173-180
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    • 2012
  • This paper proposes a novel optimization algorithm inspired by water flowing and shaking behaviors in a vessel. Water drops in our algorithm flow to the gradient descent direction and are sometimes shaken for getting out of local optimum areas when most water drops fall in local optimum areas. These flowing and shaking operations allow our algorithm to quickly approach to the global optimum without staying in local optimum areas. We experimented our algorithm with four function optimization problems and compared its results with those of particle swarm optimization. Experimental results showed that our algorithm is superior to the particle swarm optimization algorithm in terms of the speed and success ratio of finding the global optimum.

Multi-objective optimization of foundation using global-local gravitational search algorithm

  • Khajehzadeh, Mohammad;Taha, Mohd Raihan;Eslami, Mahdiyeh
    • Structural Engineering and Mechanics
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    • v.50 no.3
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    • pp.257-273
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    • 2014
  • This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, a chaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and $CO_2$ emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded $CO_2$ emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.

An Enhanced Route Optimization Scheme for Multiple LMAs in PMIPv6 Domain (다중 LMA 환경을 고려한 Proxy Mobile IP 기반의 향상된 경로 최적화 방안)

  • Jang, Jong-Min;Seo, Won-Kyeong;Choi, Jea-In;Cho, You-Ze
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.1A
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    • pp.82-89
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    • 2011
  • The Proxy Mobile IPv6 is a network-based localized mobility management protocol. In the PMIPv6, Mobile Nodes are topologically anchored at a Local Mobility Anchor, which forwards all data packets for registered Mobile Nodes. Since all data packets destined for the Mobile Nodes a1ways traverse the Mobile Nodes's Local Mobility Anchor, the LMA might be bottleneck and the end-to-end de1ay are increased. Therefore, in this paper, we proposed an enhanced Route Optimization scheme in Multiple Local Mobility Anchors environment. In order to rapid1y detect Route Optimization, we designed Domain Information Table in Mobility Access Gateway. Furthermore, we use Correspondent Binding Cache in Local Mobility Anchor to maintain Route Optimization information during Mobile Nodes's handover. To solve packet loss and reodering problems during handover, we propose a new buffering and forwarding scheme.

Design and optimization of steel trusses using genetic algorithms, parallel computing, and human-computer interaction

  • Agarwal, Pranab;Raich, Anne M.
    • Structural Engineering and Mechanics
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    • v.23 no.4
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    • pp.325-337
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    • 2006
  • A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

Hull Form Optimization using Parametric Modification Functions and Global Optimization (전역 최적화기법과 파라메트릭 변환함수를 이용한 선형 최적화)

  • Kim, Hee-Jung;Chun, Ho-Hwan;An, Nam-Hyun
    • Journal of the Society of Naval Architects of Korea
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    • v.45 no.6
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    • pp.590-600
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    • 2008
  • This paper concerns the development of a designer friendly hull form parameterization and its coupling with advanced global optimization algorithms. As optimization algorithms, we choose the Partial Swarm Optimization(PSO) recently introduced to solve global optimization problems. Most general-purpose optimization softwares used in industrial applications use gradient-based algorithms, mainly due to their convergence properties and computational efficiency when a relatively few number of variables are considered. However, local optimizers have difficulties with local minima and non-connected feasible regions. Because of the increase of computer power and of the development of efficient Global Optimization (GO) methods, in recent years nongradient-based algorithms have attracted much attention. Furthermore, GO methods provide several advantages over local approaches. In the paper, the derivative-based SQP and the GO approach PSO are compared with their relative performances in solving some typical ship design optimization problem focusing on their effectiveness and efficiency.

Study on Aerodynamic Optimization Design Process of Multistage Axial Turbine

  • Zhao, Honglei;Tan, Chunqing;Wang, Songtao;Han, Wanjin;Feng, Guotai
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • pp.130-135
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    • 2008
  • An aerodynamic optimization design process of multistage axial turbine is presented in this article: first, applying quasi-three dimensional(Q3D) design methods to conduct preliminary design and then adopting modern optimization design methods to implement multistage local optimization. Quasi-three dimensional(Q3D) design methods, which mainly refer to S2 flow surface direct problem calculation, adopt the S2 flow surface direct problem calculation program of Harbin Institute of Technology. Multistage local optimization adopts the software of Numeca/Design3D, which jointly adopts genetic algorithm and artificial neural network. The major principle of the methodology is that the successive design evaluation is performed by using an artificial neural network instead of a flow solver and the genetic algorithms may be used in an efficient way. Flow computation applies three-dimensional viscosity Navier Stokes(N-S) equation solver. Such optimization process has three features: (i) local optimization based on aerodynamic performance of every cascade; (ii) several times of optimizations being performed to every cascade; and (iii) alternate use of coarse grid and fine grid. Such process was applied to optimize a three-stage axial turbine. During the optimization, blade shape and meridional channel were respectively optimized. Through optimization, the total efficiency increased 1.3% and total power increased 2.4% while total flow rate only slightly changed. Therefore, the total performance was improved and the design objective was achieved. The preliminary design makes use of quasi-three dimensional(Q3D) design methods to achieve most reasonable parameter distribution so as to preliminarily enhance total performance. Then total performance will be further improved by adopting multistage local optimization design. Thus the design objective will be successfully achieved without huge expenditure of manpower and calculation time. Therefore, such optimization design process may be efficiently applied to the aerodynamic design optimization of multistage axial turbine.

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MULTI-STAGE AERODYNAMIC DESIGN OF AIRCRAFT GEOMETRIES BY KRIGING-BASED MODELS AND ADJOINT VARIABLE APPROACH (Kriging 기반 모델과 매개변수(Adjoint Variable)법을 이용한 항공기형상의 2단계 공력최적설계)

  • Yim, J.W.;Lee, B.J.;Kim, C.
    • 한국전산유체공학회:학술대회논문집
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    • pp.57-65
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    • 2009
  • An efficient and high-fidelity design approach for wing-body shape optimization is presented. Depending on the size of design space and the number of design of variable, aerodynamic shape optimization process is carried out via different optimization strategies at each design stage. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with a high DOF (Degree of Freedom) geometric change. For global optimization, Kriging method in conjunction with Genetic Algorithm (GA) is used. Asearching algorithm of EI (Expected Improvement) points is introduced to enhance the quality of global optimization for the wing-planform design. For local optimization, a discrete adjoint method is adopted. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The performance of the proposed design framework including wing planform design variables can be efficiently evaluated by the drag decomposition method, which can examine the improvement of various drag components, such as induced drag, wave drag, viscous drag and profile drag.

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Study on Optimization for Scheduling of Local And Express Trains Considering the Application of High Performance Train (고성능 열차를 활용한 완급행 열차 운행 스케쥴 최적화 방안 연구)

  • Kim, Moosun;Kim, Jungtai;Ko, Kyeongjun
    • Journal of the Korean Society for Railway
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    • v.19 no.2
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    • pp.234-242
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    • 2016
  • In express operation plans for urban trains, it is effective for the reduction of the number of sidetracks to apply a high performance train that has improved acceleration/deceleration ability and a regular train to local and express trains, respectively. In this research, based on a plan to use a high performance train for a local train, an optimization methodology is suggested to reduce the number of sidetracks and the operation time of the local train simultaneously. The optimization solver applied in this research is a genetic algorithm; headway, location of sidetrack and waiting time at the sidetrack are considered as design variables in the optimization problem. Consequently, by applying this system to Seoul metro line no.7, the effect of the suggested methodology was verified by obtaining the proper optimum solution.

An Integration of Local Search and Constraint Programming for Solving Constraint Satisfaction Optimization Problems (제약 만족 최적화 문제의 해결을 위한 지역 탐색과 제약 프로그래밍의 결합)

  • Hwang, Jun-Ha
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.5
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    • pp.39-47
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    • 2010
  • Constraint satisfaction optimization problem is a kind of optimization problem involving cost minimization as well as complex constraints. Local search and constraint programming respectively have been used for solving such problems. In this paper, I propose a method to integrate local search and constraint programming to improve search performance. Basically, local search is used to solve the given problem. However, it is very difficult to find a feasible neighbor satisfying all the constraints when we use only local search. Therefore, I introduced constraint programming as a tool for neighbor generation. Through the experimental results using weighted N-Queens problems, I confirmed that the proposed method can significantly improve search performance.

Local Solution of Sequential Algorithm Using Orthogonal Arrays in Discrete Design Space (이산설계공간에서 직교배열표를 이용한 순차적 알고리듬의 국부해)

  • Yi, Jeong-Wook;Park, Gyung-Jin
    • Proceedings of the KSME Conference
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    • pp.1005-1010
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    • 2004
  • The structural optimization has been carried out in the continuous design space or in the discrete design space. Generally, available designs are discrete in design practice. But methods for discrete variables are extremely expensive in computational cost. In order to overcome this weakness, an iterative optimization algorithm was proposed for design in the discrete space, which is called as a sequential algorithm using orthogonal arrays (SOA). We focus to verify the fact that the local solution can be obtained throughout the optimization with this algorithm. The local solution is defined in discrete design space. Then the search space, which is the set of candidate values of each design variables formed by the neighborhood of current design point, is defined. It is verified that a local solution can be founded by moving sequentially the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained using the SOA algorithm

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