• Title/Summary/Keyword: multi objective genetic algorithm

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Multi-Objective Shape Optimization of an Axial Fan Blade

  • Samad, Abdus;Lee, Ki-Sang;Kim, Kwang-Yong
    • International Journal of Air-Conditioning and Refrigeration
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    • v.16 no.1
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    • pp.1-8
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    • 2008
  • Numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm(NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis is presented in this work. Reynolds-averaged Navier-Stokes(RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.

Development of a new explicit soft computing model to predict the blast-induced ground vibration

  • Alzabeebee, Saif;Jamei, Mehdi;Hasanipanah, Mahdi;Amnieh, Hassan Bakhshandeh;Karbasi, Masoud;Keawsawasvong, Suraparb
    • Geomechanics and Engineering
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    • v.30 no.6
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    • pp.551-564
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    • 2022
  • Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R2) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R2 and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

Multi-objective optimization model for urban road maintenance planning using BIM, GIS, and DCE

  • Sining LI;Zhihao REN;Yuanyuan TIAN;Jung In KIM;Li MA;Longyang HUANG
    • International conference on construction engineering and project management
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    • 2024.07a
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    • pp.807-814
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    • 2024
  • Urban road maintenance creates potential risks for both road users and workers in addition to traffic congestion and delays. The adverse effects of maintenance work could be minimized through mitigation measures of work zone layout and construction arrangement, such as reducing the dimension of work zone segments and scheduling construction during low-traffic periods. However, these measures inevitably escalate construction costs. Consequently, decision-making in urban road maintenance necessitates a balance among multiple strategic objectives to facilitate optimal development via a comprehensive road maintenance management system. This study aims to propose an integrated framework to accomplish the multiple and conflicting objectives for maximizing safety and mobility while minimizing construction costs by optimizing the work zone layout and construction sequence dynamically. The framework enables the seamless information exchange among building information modeling (BIM), geographic information system (GIS), and domain-specific computational engines (DCE), which perform interdisciplinary assessments and visualization. Subsequently, a genetic algorithm is employed to determine the optimal plan considering multiple objectives due to its versatility in resolving highly complex conflict problems.

Advanced Optimization of Reliability Based on Cost Factor and Deploying On-Line Safety Instrumented System Supporting Tool (비용 요소에 근거한 신뢰도 최적화 및 On-Line SIS 지원 도구 연구)

  • Lulu, Addis;Park, Myeongnam;Kim, Hyunseung;Shin, Dongil
    • Journal of the Korean Institute of Gas
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    • v.21 no.2
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    • pp.32-40
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    • 2017
  • Safety Instrumented Systems (SIS) have wide application area. They are of vital importance at process plants to detect the onset of hazardous events, for instance, a release of some hazardous material, and for mitigating their consequences to humans, material assets, and the environment. The integrated safety systems, where electrical, electronic, and/or programmable electronic (E/E/PE) devices interact with mechanical, pneumatic, and hydraulic systems are governed by international safety standards like IEC 61508. IEC 61508 organises its requirements according to a Safety Life Cycle (SLC). Fulfilling these requirements following the SLC can be complex without the aid of SIS supporting tools. This paper presents simple SIS support tool which can greatly help the user to implement the design phase of the safety lifecycle. This tool is modelled in the form of Android application which can be integrated with a Web-based data reading and modifying system. This tool can reduce the computation time spent on the design phase of the SLC and reduce the possible errors which can arise in the process. In addition, this paper presents an optimization approach to SISs based on cost measures. The multi-objective genetic algorithm has been used for the optimization to search for the best combinations of solutions without enumeration of all the solution space.

Multi-objective Genetic Algorism Model for Determining an Optimal Capital Structure of Privately-Financed Infrastructure Projects (민간투자사업의 최적 자본구조 결정을 위한 다목적 유전자 알고리즘 모델에 관한 연구)

  • Yun, Sungmin;Han, Seung Heon;Kim, Du Yon
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.28 no.1D
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    • pp.107-117
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    • 2008
  • Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.

Multi-FNN Identification by Means of HCM Clustering and ITs Optimization Using Genetic Algorithms (HCM 클러스터링에 의한 다중 퍼지-뉴럴 네트워크 동정과 유전자 알고리즘을 이용한 이의 최적화)

  • 오성권;박호성
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.5
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    • pp.487-496
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    • 2000
  • In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

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Optimum maintenance scenario generation for existing steel-girder bridges based on lifetime performance and cost

  • Park, Kyung Hoon;Lee, Sang Yoon;Yoon, Jung Hyun;Cho, Hyo Nam;Kong, Jung Sik
    • Smart Structures and Systems
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    • v.4 no.5
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    • pp.641-653
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    • 2008
  • This paper proposes a practical and realistic method to establish an optimal lifetime maintenance strategy for deteriorating bridges by considering the life-cycle performance as well as the life-cycle cost. The proposed method offers a set of optimal tradeoff maintenance scenarios among other conflicting objectives, such as minimizing cost and maximizing performance. A genetic algorithm is used to generate a set of maintenance scenarios that is a multi-objective combinatorial optimization problem related to the lifetime performance and the life-cycle cost as separate objective functions. A computer program, which generates optimal maintenance scenarios, was developed based on the proposed method using the life-cycle costs and the performance of bridges. The subordinate relation between bridge members has been considered to decide optimal maintenance sequence and a corresponding algorithm has been implemented into the program. The developed program has been used to present a procedure for finding an optimal maintenance scenario for steel-girder bridges on the Korean National Road. Through this bridge maintenance scenario analysis, it is expected that the developed method and program can be effectively used to allow bridge managers an optimal maintenance strategy satisfying various constraints and requirements.

Multi-Objective Pareto Optimization of Parallel Synthesis of Embedded Computer Systems

  • Drabowski, Mieczyslaw
    • International Journal of Computer Science & Network Security
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    • v.21 no.3
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    • pp.304-310
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    • 2021
  • The paper presents problems of optimization of the synthesis of embedded systems, in particular Pareto optimization. The model of such a system for its design for high-level of abstract is based on the classic approach known from the theory of task scheduling, but it is significantly extended, among others, by the characteristics of tasks and resources as well as additional criteria of optimal system in scope structure and operation. The metaheuristic algorithm operating according to this model introduces a new approach to system synthesis, in which parallelism of task scheduling and resources partition is applied. An algorithm based on a genetic approach with simulated annealing and Boltzmann tournaments, avoids local minima and generates optimized solutions. Such a synthesis is based on the implementation of task scheduling, resources identification and partition, allocation of tasks and resources and ultimately on the optimization of the designed system in accordance with the optimization criteria regarding cost of implementation, execution speed of processes and energy consumption by the system during operation. This paper presents examples and results for multi-criteria optimization, based on calculations for specifying non-dominated solutions and indicating a subset of Pareto solutions in the space of all solutions.

Optimal Design of a Novel Knee Orthosis using a Genetic Algorism (유전자 알고리즘을 이용한 새로운 무릎 보장구의 최적 설계)

  • Pyo, Sang-Hun;Yoon, Jung-Won
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.10
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    • pp.1021-1028
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    • 2011
  • The objective of this paper is to optimize the design parameters of a novel mechanism for a robotic knee orthosis. The feature of the proposed knee othosis is to drive a knee joint with independent actuation during swing and stance phases, which can allow an actuator with fast rotation to control swing motions and an actuator with high torque to control stance motions, respectively. The quadriceps device operates in five-bar links with 2-DOF motions during swing phase and is changed to six-bar links during stance phase by the contact motion to the patella device. The hamstring device operates in a slider-crank mechanism for entire gait cycle. The suggested kinematic model will allow a robotic knee orthosis to use compact and light actuators with full support during walking. However, the proposed orthosis must use additional linkages than a simple four-bar mechanism. To maximize the benefit of reducing the actuators power by using the developed kinematic design, it is necessary to minimize total weight of the device, while keeping necessary actuator performances of torques and angular velocities for support. In this paper, we use a SGA (Simple Genetic Algorithm) to minimize sum of total link lengths and motor power by reducing the weight of the novel knee orthosis. To find feasible parameters, kinematic constraints of the hamstring and quadriceps mechanisms have been applied to the algorithm. The proposed optimization scheme could reduce sum of total link lengths to half of the initial value. The proposed optimization scheme can be applied to reduce total weight of general multi-linkages while keeping necessary actuator specifications.

Propulsion System Design and Optimization for Ground Based Interceptor using Genetic Algorithm

  • Qasim, Zeeshan;Dong, Yunfeng;Nisar, Khurram
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2008.03a
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    • pp.330-339
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    • 2008
  • Ground-based interceptors(GBI) comprise a major element of the strategic defense against hostile targets like Intercontinental Ballistic Missiles(ICBM) and reentry vehicles(RV) dispersed from them. An optimum design of the subsystems is required to increase the performance and reliability of these GBI. Propulsion subsystem design and optimization is the motivation for this effort. This paper describes an effort in which an entire GBI missile system, including a multi-stage solid rocket booster, is considered simultaneously in a Genetic Algorithm(GA) performance optimization process. Single goal, constrained optimization is performed. For specified payload and miss distance, time of flight, the most important component in the optimization process is the booster, for its takeoff weight, time of flight, or a combination of the two. The GBI is assumed to be a multistage missile that uses target location data provided by two ground based RF radar sensors and two low earth orbit(LEO) IR sensors. 3Dimensional model is developed for a multistage target with a boost phase acceleration profile that depends on total mass, propellant mass and the specific impulse in the gravity field. The monostatic radar cross section (RCS) data of a three stage ICBM is used. For preliminary design, GBI is assumed to have a fixed initial position from the target launch point and zero launch delay. GBI carries the Kill Vehicle(KV) to an optimal position in space to allow it to complete the intercept. The objective is to design and optimize the propulsion system for the GBI that will fulfill mission requirements and objectives. The KV weight and volume requirements are specified in the problem definition before the optimization is computed. We have considered only continuous design variables, while considering discrete variables as input. Though the number of stages should also be one of the design variables, however, in this paper it is fixed as three. The elite solution from GA is passed on to(Sequential Quadratic Programming) SQP as near optimal guess. The SQP then performs local convergence to identify the minimum mass of the GBI. The performance of the three staged GBI is validated using a ballistic missile intercept scenario modeled in Matlab/SIMULINK.

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