• 제목/요약/키워드: Multi-objective function optimization

검색결과 273건 처리시간 0.021초

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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    • 제17권4호
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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.

Trade-off Analysis in Multi-objective Optimization Using Chebyshev Orthogonal Polynomials

  • Baek Seok-Heum;Cho Seok-Swoo;Kim Hyun-Su;Joo Won-Sik
    • Journal of Mechanical Science and Technology
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    • 제20권3호
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    • pp.366-375
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    • 2006
  • In this paper, it is intended to introduce a method to solve multi-objective optimization problems and to evaluate its performance. In order to verify the performance of this method it is applied for a vertical roller mill for Portland cement. A design process is defined with the compromise decision support problem concept and a design process consists of two steps: the design of experiments and mathematical programming. In this process, a designer decides an object that the objective function is going to pursuit and a non-linear optimization is performed composing objective constraints with practical constraints. In this method, response surfaces are used to model objectives (stress, deflection and weight) and the optimization is performed for each of the objectives while handling the remaining ones as constraints. The response surfaces are constructed using orthogonal polynomials, and orthogonal array as design of experiment, with analysis of variance for variable selection. In addition, it establishes the relative influence of the design variables in the objectives variability. The constrained optimization problems are solved using sequential quadratic programming. From the results, it is found that the method in this paper is a very effective and powerful for the multi-objective optimization of various practical design problems. It provides, moreover, a reference of design to judge the amount of excess or shortage from the final object.

실험계획법과 수리적방법을 이용한 이산설계 공간에서의 다목적 최적설계 (Multi-objective Optimization in Discrete Design Space using the Design of Experiment and the Mathematical Programming)

  • 이동우;백석흠;이경영;조석수;주원식
    • 대한기계학회논문집A
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    • 제26권10호
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    • pp.2150-2158
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    • 2002
  • A recent research and development has the requirement for the optimization to shorten design time of modified or new product model and to obtain more precise engineering solution. General optimization problem must consider many conflicted objective functions simultaneously. Multi-objective optimization treats the multiple objective functions and constraints with design change. But, real engineering problem doesn't describe accurate constraint and objective function owing to the limit of representation. Therefore this study applies variance analysis on the basis of structure analysis and DOE to the vertical roller mill fur portland cement and proposed statistical design model to evaluate the effect of structural modification with design change by performing practical multi-objective optimization considering mass, stress and deflection.

Fuzzy Preference Based Interactive Fuzzy Physical Programming and Its Application in Multi-objective Optimization

  • Zhang Xu;Huang Hong-Zhong;Yu Lanfeng
    • Journal of Mechanical Science and Technology
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    • 제20권6호
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    • pp.731-737
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    • 2006
  • Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer.

구조물에 대한 다목적퍼지최적화 (Multi-Objective Fuzzy Optimization of Structures)

  • 박춘욱;편해완;강문명
    • 한국강구조학회 논문집
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    • 제12권5호통권48호
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    • pp.503-513
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    • 2000
  • 본 연구에서는 구조물의 최적설계문제를 다를 때 나타나는 퍼지성을 고려하는 동시에 대립되는 기준들을 다루기 위해 중요도를 적용 유전자알고리즘 및 퍼지이론에 의한 이산형의 다목적 함수를 갖는 트러스구조물의 최적화를 시도하는 다목적 이산화 최적화 프로그램을 개발하였다. 그리고 개발된 프로그램을 적용하여 10부재철골트러스에 대한 설계 예를 들어 비교 고찰하였다. 본 연구를 통해 평면트러스구조물에대한 응력해석 및 최적설계가 일률적으로 처리될 수 있는 통합 시스템화된 퍼지-유전자알고리즘에 의한 다목적최적 구조설계가 가능하게 되었다. 특히 일반최적설계에서 처리되지 않는 불확실한 제약조건에 대한 경우에 대하여도 피지이론을 도입함으로써 가능하게 되어 보다 구조물의 합리적인 최적설계가 가능하게 되었다.

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Structural damage identification of truss structures using self-controlled multi-stage particle swarm optimization

  • Das, Subhajit;Dhang, Nirjhar
    • Smart Structures and Systems
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    • 제25권3호
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    • pp.345-368
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    • 2020
  • The present work proposes a self-controlled multi-stage optimization method for damage identification of structures utilizing standard particle swarm optimization (PSO) algorithm. Damage identification problem is formulated as an inverse optimization problem where damage severity in each element of the structure is considered as optimization variables. An efficient objective function is formed using the first few frequencies and mode shapes of the structure. This objective function is minimized by a self-controlled multi-stage strategy to identify and quantify the damage extent of the structural members. In the first stage, standard PSO is utilized to get an initial solution to the problem. Subsequently, the algorithm identifies the most damage-prone elements of the structure using an adaptable threshold value of damage severity. These identified elements are included in the search space of the standard PSO at the next stage. Thus, the algorithm reduces the dimension of the search space and subsequently increases the accuracy of damage prediction with a considerable reduction in computational cost. The efficiency of the proposed method is investigated and compared with available results through three numerical examples considering both with and without noise. The obtained results demonstrate the accuracy of the present method can accurately estimate the location and severity of multi-damage cases in the structural systems with less computational cost.

Response surface methodology based multi-objective optimization of tuned mass damper for jacket supported offshore wind turbine

  • Rahman, Mohammad S.;Islam, Mohammad S.;Do, Jeongyun;Kim, Dookie
    • Structural Engineering and Mechanics
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    • 제63권3호
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    • pp.303-315
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    • 2017
  • This paper presents a review on getting a Weighted Multi-Objective Optimization (WMO) of Tuned Mass Damper (TMD) parameters based on Response Surface Methodology (RSM) coupled central composite design and Weighted Desirability Function (WDF) to attenuate the earthquake vibration of a jacket supported Offshore Wind Turbine (OWT). To optimize the parameters (stiffness and damping coefficient) of damper, the frequency ratio and damping ratio were considered as a design variable and the top displacement and frequency response were considered as objective functions. The optimization has been carried out under only El Centro earthquake results and after obtained the optimal parameters, more two earthquakes (California and Northridge) has been performed to investigate the performance of optimal damper. The obtained results also compared with the different conventional TMD's designed by Den Hartog's, Sadek et al.'s and Warburton's method. From the results, it was found that the optimal TMD based on RSM shows better response than the conventional damper. It is concluded that the proposed response model offers an efficient approach regarding the TMD optimization.

Design and Field Test of an Optimal Power Control Algorithm for Base Stations in Long Term Evolution Networks

  • Zeng, Yuan;Xu, Jing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권12호
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    • pp.5328-5346
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    • 2016
  • An optimal power control algorithm based on convex optimization is proposed for base stations in long term evolution networks. An objective function was formulated to maximize the proportional fairness of the networks. The optimal value of the objective function was obtained using convex optimization and distributed methods based on the path loss model between the base station and users. Field tests on live networks were conducted to evaluate the performance of the proposed algorithm. The experimental results verified that, in a multi-cell multi-user scenario, the proposed algorithm increases system throughputs, proportional fairness, and energy efficiency by 9, 1.31 and 20.2 %, respectively, compared to the conventional fixed power allocation method.

Structural Optimization of a Thick-Walled Composite Multi-Cell Wing Box Using an Approximation Method

  • Kim, San-Hui;Kim, Pyung-Hwa;Kim, Myung-Jun;Park, Jung-sun
    • 항공우주시스템공학회지
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    • 제15권2호
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    • pp.1-9
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    • 2021
  • In this paper, a thickness compensation function is introduced to consider the shear deformation and warping effect resulting from increased thickness in the composite multi-cell wing box. The thickness compensation function is used to perform the structure optimization of the multi-cell. It is determined by minimizing the error of an analytical formula using solid mechanics and the Ritz method. It is used to define a structural performance prediction expression due to the increase in thickness. The parameter is defined by the number of spars and analyzed by the critical buckling load and the limited failure index as a response. Constraints in structural optimization are composed of displacements, torsional angles, the critical buckling load, and the failure index. The objective function is the mass, and its optimization is performed using a genetic algorithm.

Optimal placement of piezoelectric actuators and sensors on a smart beam and a smart plate using multi-objective genetic algorithm

  • Nestorovic, Tamara;Trajkov, Miroslav;Garmabi, Seyedmehdi
    • Smart Structures and Systems
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    • 제15권4호
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    • pp.1041-1062
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    • 2015
  • In this paper a method of finding optimal positions for piezoelectric actuators and sensors on different structures is presented. The genetic algorithm and multi-objective genetic algorithm are selected for optimization and $H_{\infty}$ norm is defined as a cost function for the optimization process. To optimize the placement concerning the selected modes simultaneously, the multi-objective genetic algorithm is used. The optimization is investigated for two different structures: a cantilever beam and a simply supported plate. Vibrating structures are controlled in a closed loop with feedback gains, which are obtained using optimal LQ control strategy. Finally, output of a structure with optimized placement is compared with the output of the structure with an arbitrary, non-optimal placement of piezoelectric patches.