• Title/Summary/Keyword: Algorithm optimization

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A Feature-based Reconstruction Algorithm for Structural Optimization (구조 최적화를 위한 특징형상 재설계 알고리즘)

  • Park, Sangkun
    • Journal of Institute of Convergence Technology
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    • v.4 no.2
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    • pp.1-9
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    • 2014
  • This paper examines feature-based reconstruction algorithm using feature-based modeling and based on topology optimization technology, which aims to achieve a minimal volume weight and to satisfy user-defined constraints such as stress, deformation related conditions. The finite element model after topology optimization allows us to remove some region of a solid model for predefined volume requirement. The stress or deformation distribution resulted from finite element analysis enables us to add some material to the solid model for a robust structure. For this purpose, we propose a feature-based redesign algorithm which inserts negative features to the solid model for material removal and positive features for material addition, and we introduce a bisection method which searches an optimal structure by iteratively applying the feature-based redesign algorithm. Several examples are considered to illustrate the proposed algorithms and to demonstrate the effectiveness of the present approach.

Buckling load optimization of laminated plates via artificial bee colony algorithm

  • Topal, Umut;Ozturk, Hasan Tahsin
    • Structural Engineering and Mechanics
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    • v.52 no.4
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    • pp.755-765
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    • 2014
  • In this present work, Artificial Bee Colony Algorithm (ABCA) is used to optimize the stacking sequences of simply supported antisymmetric laminated composite plates with criticial buckling load as the objective functions. The fibre orientations of the layers are selected as the optimization design variables with the aim to find the optimal laminated plates. In order to perform the optimization based on the ABCA, a special code is written in MATLAB software environment. Several numerical examples are presented to illustrate this optimization algorithm for different plate aspect ratios, number of layers and load ratios.

Sloshing Reduction Optimization of Storage Tank Using Evolutionary Method (진화적 기법을 이용한 유체저장탱크의 슬로싱 저감 최적화)

  • 김현수;이영신;김승중;김영완
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2004.05a
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    • pp.410-415
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    • 2004
  • The oscillation of the fluid caused by external forces is call ed sloshing, which occurs in moving vehicles with contained liquid masses, such as trucks, railroad cars, aircraft, and liquid rocket. This sloshing effect could be a severe problem in vehicle stability and control. In this study, the optimization design technique for reduction of the sloshing using evolutionary method is suggested. Two evolutionary methods are employed, respectively the artificial neural network(ANN) and genetic algorithm. An artificial neural network is used for the analysis of sloshing and genetic algorithm is adopted as optimization algorithm. As a result of optimization design, the optimized size and location of the baffle is presented

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Multi-swarm fruit fly optimization algorithm for structural damage identification

  • Li, S.;Lu, Z.R.
    • Structural Engineering and Mechanics
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    • v.56 no.3
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    • pp.409-422
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    • 2015
  • In this paper, the Multi-Swarm Fruit Fly Optimization Algorithm (MFOA) is presented for structural damage identification using the first several natural frequencies and mode shapes. We assume damage only leads to the decrease of element stiffness. The differences on natural frequencies and mode shapes of damaged and intact state of a structure are used to establish the objective function, which transforms a damage identification problem into an optimization problem. The effectiveness and accuracy of MFOA are demonstrated by three different structures. Numerical results show that the MFOA has a better capacity for structural damage identification than the original Fruit Fly Optimization Algorithm (FOA) does.

OPTIMUM DESIGN OF AN AUTOMOTIVE CATALYTIC CONVERTER FOR MINIMIZATION OF COLD-START EMISSIONS USING A MICRO GENETIC ALGORITHM

  • Kim, Y.D.;Kim, W.S.
    • International Journal of Automotive Technology
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    • v.8 no.5
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    • pp.563-573
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    • 2007
  • Optimal design of an automotive catalytic converter for minimization of cold-start emissions is numerically performed using a micro genetic algorithm for two optimization problems: optimal geometry design of the monolith for various operating conditions and optimal axial catalyst distribution. The optimal design process considered in this study consists of three modules: analysis, optimization, and control. The analysis module is used to evaluate the objective functions with a one-dimensional single channel model and the Romberg integration method. It obtains new design variables from the control module, produces the CO cumulative emissions and the integral value of a catalyst distribution function over the monolith volume, and provides objective function values to the control module. The optimal design variables for minimizing the objective functions are determined by the optimization module using a micro genetic algorithm. The control module manages the optimal design process that mainly takes place in both the analysis and optimization modules.

Optimum design of multi-span composite box girder bridges using Cuckoo Search algorithm

  • Kaveh, A.;Bakhshpoori, T.;Barkhori, M.
    • Steel and Composite Structures
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    • v.17 no.5
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    • pp.705-719
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    • 2014
  • Composite steel-concrete box girders are frequently used in bridge construction for their economic and structural advantages. An integrated metaheuristic based optimization procedure is proposed for discrete size optimization of straight multi-span steel box girders with the objective of minimizing the self-weight of girder. The metaheuristic algorithm of choice is the Cuckoo Search (CS) algorithm. The optimum design of a box girder is characterized by geometry, serviceability and ultimate limit states specified by the American Association of State Highway and Transportation Officials (AASHTO). Size optimization of a practical design example investigates the efficiency of this optimization approach and leads to around 15% of saving in material.

An improved algorithm in railway truss bridge optimization under stress, displacement and buckling constraints imposed on moving load

  • Mohammadzadeh, Saeed;Nouri, Mehrdad
    • Structural Engineering and Mechanics
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    • v.46 no.4
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    • pp.571-594
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    • 2013
  • Railway truss bridges are amongst the essential structures in railway transportation. Minimization of the construction and maintenance costs of these trusses can effectively reduce investments in railway industries. In case of railway bridges, due to high ratio of the live load to the dead load, the moving load has considerable influence on the bridge dynamics. In this paper, optimization of the railway truss bridges under moving load is taken into consideration. The appropriate algorithm namely Hyper-sphere algorithm is used for this multifaceted problem. Through optimization the efficiency of the method successfully raised about 5 percent, compared with similar algorithms. The proposed optimization carried out on several typical railway trusses. The influences of buckling, deformation constraints, and the optimum height of each type of truss, assessed using a simple approximation method.

Symbiotic organisms search algorithm based solution to optimize both real power loss and voltage stability limit of an electrical energy system

  • Pagidi, Balachennaiah;Munagala, Suryakalavathi;Palukuru, Nagendra
    • Advances in Energy Research
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    • v.4 no.4
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    • pp.255-274
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    • 2016
  • This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

Performance Evaluation and Parametric Study of MGA in the Solution of Mathematical Optimization Problems (수학적 최적화 문제를 이용한 MGA의 성능평가 및 매개변수 연구)

  • Cho, Hyun-Man;Lee, Hyun-Jin;Ryu, Yeon-Sun;Kim, Jeong-Tae;Na, Won-Bae;Lim, Dong-Joo
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2008.04a
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    • pp.416-421
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    • 2008
  • A Metropolis genetic algorithm (MGA) is a newly-developed hybrid algorithm combining simple genetic algorithm (SGA) and simulated annealing (SA). In the algorithm, favorable features of Metropolis criterion of SA are incorporated in the reproduction operations of SGA. This way, MGA alleviates the disadvantages of finding imprecise solution in SGA and time-consuming computation in SA. It has been successfully applied and the efficiency has been verified for the practical structural design optimization. However, applicability of MGA for the wider range of problems should be rigorously proved through the solution of mathematical optimization problems. Thus, performances of MGA for the typical mathematical problems are investigated and compared with those of conventional algorithms such as SGA, micro genetic algorithm (${\mu}GA$), and SA. And, for better application of MGA, the effects of acceptance level are also presented. From numerical Study, it is again verified that MGA is more efficient and robust than SA, SGA and ${\mu}GA$ in the solution of mathematical optimization problems having various features.

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Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling

  • Tianhao Zhao;Linjie Wu;Di Wu;Jianwei Li;Zhihua Cui
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.4
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    • pp.1100-1122
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    • 2023
  • Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.