• Title/Summary/Keyword: Space Optimization

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Design of IG-based Fuzzy Models Using Improved Space Search Algorithm (개선된 공간 탐색 알고리즘을 이용한 정보입자 기반 퍼지모델 설계)

  • Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.6
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    • pp.686-691
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    • 2011
  • This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

Weighting objectives strategy in multicriterion fuzzy mechanical and structural optimization

  • Shih, C.J.;Yu, K.C.
    • Structural Engineering and Mechanics
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    • v.3 no.4
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    • pp.373-382
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    • 1995
  • The weighting strategy has received a great attention and has been widely applied to multicriterion optimization. This gaper examines a global criterion method (GCM) with the weighting objectives strategy in fuzzy structural engineering problems. Fuzziness of those problems are in their design goals, constraints and variables. Most of the constraints are originated from analysis of engineering mechanics. The GCM is verified to be equivalent to fuzzy goal programming via a truss design. Continued and mixed discrete variable spaces are presented and examined using a fuzzy global criterion method (FGCM). In the design process a weighting parameter with fuzzy information is introduced into the design and decision making. We use a uniform machine-tool spindle as an illustrative example in continuous design space. Fuzzy multicriterion optimization in mixed design space is illustrated by the design of mechanical spring stacks. Results show that weighting strategy in FGCM can generate both the best compromise solution and a set of Pareto solutions in fuzzy environment. Weighting technique with fuzziness provides a more relaxed design domain, which increases the satisfying degree of a compromise solution or improves the final design.

Study of Supporting Location Optimization for a Structure under Non-uniform Load Using Genetic Algorithm (유전알고리즘을 이용한 비균일 하중을 받는 구조물의 지지 위치 최적화 연구)

  • Kim, G.H.;Lee, Y.S.;Kim, H.K.;Her, N.I.;Sa, J.W.;Yang, H.L.;Kim, B.C.;Bak, J.S.
    • Proceedings of the KSME Conference
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    • 2003.11a
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    • pp.1322-1327
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    • 2003
  • It is important to determine supporting locations for structural stability of a structure under non-uniform load in space interfered by other parts. In this case, There are many local optima with discontinuous design space. Therefore, The traditional optimization methods based on derivative are not suitable. Whereas, Genetic algorithm(GA) based on stochastic search technique is a very robust and general method. This paper has been presented to determine supporting locations of the vertical supports for reducing stress of the KSTAR(Korea super Superconducting Tokamak Advanced Research) IVCC(In-vessel control coil) under non-uniform electromagnetic load and space interfered by other parts using genetic algorithm. For this study, we develop a program combining finite element analysis with a genetic algorithm to perform structural analysis of IVCC. In addition, this paper presents a technique to perform optimization with FEM when design variables are trapped in an incongruent design space.

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A robust multi-objective localized outrigger layout assessment model under variable connecting control node and space deposition

  • Lee, Dongkyu;Lee, Jaehong;Kang, Joowon
    • Steel and Composite Structures
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    • v.33 no.6
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    • pp.767-776
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    • 2019
  • In this article, a simple and robust multi-objective assessment method to control design angles and node positions connected among steel outrigger truss members is proposed to approve both structural safety and economical cost. For given outrigger member layouts, the present method utilizes general-purpose prototypes of outrigger members, having resistance to withstand lateral load effects directly applied to tall buildings, which conform to variable connecting node and design space deposition. Outrigger layouts are set into several initial design conditions of height to width of an arbitrary given design space, i.e., variable design space. And then they are assessed in terms of a proposed multi-objective function optimizing both minimal total displacement and material quantity subjected to design impact factor indicating the importance of objectives. To evaluate the proposed multi-objective function, an analysis model uses a modified Maxwell-Mohr method, and an optimization model is defined by a ground structure assuming arbitrary discrete straight members. It provides a new robust assessment model from a local design point of view, as it may produce specific optimal prototypes of outrigger layouts corresponding to arbitrary height and width ratio of design space. Numerical examples verify the validity and robustness of the present assessment method for controlling prototypes of outrigger truss members considering a multi-objective optimization achieving structural safety and material cost.

Web Services-based Multidisciplinary Design Optimization System (웹 서비스 기반 MDO 시스템)

  • Lee, Ho-Jun;Lee, Jae-Woo;Lee, Jeong-Oog
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.35 no.12
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    • pp.1121-1128
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    • 2007
  • MDO(Multidisciplinary Design and Optimization) can be applied for design of complex systems such as aircraft and SLV(Space Launch Vehicle). MDO System can be an integrated environment or a system, which is for synthetic and instantaneous analysis and design optimization in various design fields. MDO System has to efficiently use and integrate distributed resources such as various analysis codes, optimization codes, CAD, DBMS, GUI, and etc. in heterogeneous environments. In this paper, we present Web Services-based MDO System that integrates resources for MDO using Globus Toolkit and provides organic autonomous execution using automation technique such as Workflow system and agent. And also, it provides collaborative design environment through web user interfaces.

Simulated Annealing Algorithm for Optimum Design of Space Truss Structures (입체 트러스구조물의 최적설계를 위한 SA기법)

  • 정제원;박효선
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 1999.04a
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    • pp.102-109
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    • 1999
  • Two phase simulated annealing algorithm is presented as a structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. Univariate searching algorithm is adopted for automatic selection of initial values of design variables for SA algorithm. The proper values of cooling factors and reasonable stopping criteria for optimum design of space truss structures are proposed to enhance the performance of optimization process. Optimum weights and design solutions are presented for two well-blown example structures and compared with those reported in the literature.

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An optimization framework to tackle challenging cargo accommodation tasks in space engineering

  • Fasano, Giorgio;Gastaldi, Cristina;Piras, Annamaria;Saia, Dario
    • Advances in aircraft and spacecraft science
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    • v.1 no.2
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    • pp.197-218
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    • 2014
  • Quite a demanding task frequently arises in space engineering, when dealing with the cargo accommodation of modules and vehicles. The objective of this effort usually aims at maximizing the loaded cargo, or, at least, at meeting the logistic requirements posed by the space agencies. Complex accommodation rules are supposed to be taken into account, in compliance with strict balancing conditions and very tight operational restrictions. The context of the International Space Station (ISS) has paved the way for a relevant research and development activity, providing the company with a remarkable expertise in the field. CAST (Cargo Accommodation Support Tool) is a dedicated in-house software package (funded by the European Space Agency, ESA, and achieved by Thales Alenia Space), to carry out the whole loading of the Automated Transfer Vehicle (ATV). An ad hoc version, tailored to the Columbus (ISS attached laboratory) on-board stowage issue, has been further implemented and is to be used from now on. This article surveys the overall approach followed, highlighting the advantages of the methodology put forward, both in terms of solution quality and time saving, through an overview of the outcomes obtained to date. Insights on possible extensions to further space applications, especially in the perspective of the paramount challenges of the near future, are, in addition, presented.

An optimized deployment strategy of smart smoke sensors in a large space

  • Liu, Pingshan;Fang, Junli;Huang, Hongjun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.11
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    • pp.3544-3564
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    • 2022
  • With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

Optimization of Gable Frame Using the Modified Genetic Algorithm (개선된 유전자 알고리즘을 이용한 산형 골조의 최적화)

  • Lee, Hong-Woo
    • Journal of Korean Association for Spatial Structures
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    • v.3 no.4 s.10
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    • pp.59-67
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    • 2003
  • Genetic algorithm is one of the best ways to solve a discrete variable optimization problem. Genetic algorithm tends to thrive in an environment in which the search space is uneven and has many hills and valleys. In this study, genetic algorithm is used for solving the design problem of gable structure. The design problem of frame structure has some special features(complicate design space, many nonlinear constrants, integer design variables, termination conditions, special information for frame members, etc.), and these features must be considered in the formulation of optimization problem and the application of genetic algorithm. So, 'FRAME operator', a new genetic operator for solving the frame optimization problem effectively, is developed and applied to the design problem of gable structure. This example shows that the new opreator has the possibility to be an effective frame design operator and genetic algorithm is suitable for the frame optimization problem.

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Performance Analysis of an Aircraft Gas Turbine Engine using Particle Swarm Optimization

  • Choi, Jae Won;Sung, Hong-Gye
    • International Journal of Aeronautical and Space Sciences
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    • v.15 no.4
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    • pp.434-443
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    • 2014
  • A turbo fan engine performance analysis and the optimization using particle swarm optimization(PSO) algorithm have been conducted to investigate the effects of major performance design parameters of an aircraft gas turbine engine. The FJ44-2C turbofan engine, which is widely used in the small business jet, CJ2 has been selected as the basic model. The design parameters consists of the bypass ratio, burner exit temperature, HP compressor ratio, fan inlet mass flow, and nozzle cooling air ratio. The sensitivity analysis of the parameters has been evaluated and the optimization of the parameters has been performed to achieve high net thrust or low specific fuel consumption.