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An integrated multidisciplinary design optimization method for computer numerical control machine tool development

ADVANCES IN MECHANICAL ENGINEERING 2015年第2期7卷 1-9页

作者： Zhang, Zaifang Song, Jianjun Liu, Yuan Xu, Danhua Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

Computer numerical control machine tool is a typical complex product related with multidisciplinary fields, complex structure, and high-performance requirements. It is difficult to identify the overall optimal solution of the machine tool structure for their multiple objectives. A new integrated multidisciplinary design optimization method is then proposed by using a Latin hypercube sampling, a Kriging approximate model, and a multi-objective genetic algorithm. Design space and parametric model are built by choosing appropriate design variables and their value ranges. Samples in design space are generated by optimal Latin hypercube method, and design variable contributions for design performance are discussed for aiding the designer's judgments. The Kriging model is built by using polynomial approximation according to the response outputs of these samples. The multidisciplinary design model is established based on three optimization objectives, that is, setting mass, optimum deformation, and first-order natural frequency, and two constraints, that is, second-order natural frequency and third-order natural frequency. The optimal solution is identified by using a multi-objective genetic algorithm. The proposed method is applied in a multidisciplinary optimization case study for a typical computer numerical control machine tool. In the optimal solution, the mass decreases by 3.35% and the first-order natural frequency increases by 4.34% in contrast to the original solution.

关键词： Computer numerical control machine tool multidisciplinary optimization Latin hypercube sampling Kriging approximate model multi-objective genetic algorithm

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A Two-Stage multi-objective genetic-Fuzzy Mining algorithm

A Two-Stage Multi-Objective Genetic-Fuzzy Mining Algorithm

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6th IEEE International Workshop on genetic and Evolutionary Fuzzy Systems (GEFS)

作者： Chen, Chun-Hao He, Ji-Syuan Hong, Tzung-Pei Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781467358996

In this paper, we propose a two-stage multi-objective fuzzy mining algorithm for dealing with linguistic knowledge discovery. In the first stage, the multi-objective genetic algorithm is used to derive a set of non-dominated membership functions (Pareto solutions) with two objective functions. In the second stage, the clustering technique is utilized to find representative solutions from the Pareto solutions. The representative solutions could be employed to mine fuzzy association rules according to the favorites of decision makers. Experiments on a simulation dataset are made and the results show the effectiveness of the proposed algorithm.

关键词： multi-objective genetic algorithm clustering technique membership function taxonomy fuzzy association rule

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A Cluster-Based Orthogonal multi-objective genetic algorithm

A Cluster-Based Orthogonal Multi-Objective Genetic Algorithm

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4th International Symposium on Intelligence Computation and Applications

作者： Zhu, Jiankai Dai, Guangming Mo, Li China Univ Geosci Wuhan 430074 Peoples R China

ISBN: (纸本)9783642049613

multi-objective genetic algorithm is proved to be suitable for solving multi-objective optimization problems. However, it is usually very hard to balance the convergence and diversity of a multi-objective genetic algorithm. This paper introduces a new algorithm, with both good convergence and diversity based on clustering method and multi-parent crossover operator. Meanwhile, an initial population is generated by orthogonal design to enhance the search effort of the algorithm. The experimental results on a number of test problems indicate the good performance of the Cluster-Based Orthogonal multi-objective genetic algorithm.

关键词： multi-objective Optimization Problem multi-objective genetic algorithm Orthogonal design Cluster multi-parent Crossover

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genetic algorithm INTEGRATED SLIDING MODE CONTROL OF A VEHICLE 11

GENETIC ALGORITHM INTEGRATED SLIDING MODE CONTROL OF A VEHIC...

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11th World Congress on Computational Mechanics (WCCM) / 5th European Conference on Computational Mechanics (ECCM) / 6th European Conference on Computational Fluid Dynamics (ECFD)

作者： Ozer, Hasan O. Sayin, Alaattin Korkmaz, Nuray Yagiz, Nurkan Istanbul Univ Vocat Sch Tech Sci Programme Air Conditioning & Refrigerat Technol TR-34320 Istanbul Turkey Istanbul Univ Vocat Sch Tech Sci Programme Biomed Equipment Tech TR-34320 Istanbul Turkey Istanbul Univ Fac Engn Dept Mech Engn TR-34320 Istanbul Turkey

ISBN: (纸本)9788494284472

The main propose of suspension systems is to isolate car body motion from the road excitations and improve the ride comfort. Hence, to control of suspension system is important both comfort and road holding. The aim of this study is to design Sliding Mode Controller tuned by genetic algorithm (GA) for providing smooth vertical motion of car body. Sliding mode control (SMC) has been used in many mechanical and structural systems due to its robustness, simplicity and high control performance. However, tuning optimum controller parameters for systems is still an important research area. The proposed SMC parameters have been tuned by GA with several fitness functions to get better dynamic performance. The vehicle model is excited by random road data. Then, simulation results of uncontrolled and GA integrated sliding mode controllers models are compared. The results show that vehicle model with SMC tuned by GA is effective to decrease the effects of road induced vibrations.

关键词： Sliding Mode Controller genetic algorithm multi-objective genetic algorithm Vehicle Model Simulation of Vehicle Vibrations

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A multi-objective genetic algorithm Method to Support multi-Agent Negotiations

A Multi-Objective Genetic Algorithm Method to Support Multi-...

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2nd International Conference on Future Information Technology and Management Engineering

作者： Beheshti, R. Rahmani, A. T. Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9780769538808

Negotiations are among the most common ways that agents in a multi-agent system use to reach agreements. Because negotiations commonly are multi-lateral and multi-issue, these processes become more difficult. In the real world applications this becomes more important where the autonomous agents involved in a negotiation should reach maximum payoff in minimum time. In this work a new negotiation mechanism is proposed that is based on the multi-objective genetic algorithms. Several measures are defined that can show fitness of an offer in the set of feasible offers that an agent can have in each round of negotiations. The results show that this method can be used in real applications and is competitive with existing approaches.

关键词： Negotiation Autonomous agent multi-objective genetic algorithm

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Real-time online Compensation the Nonlinearities and Uncertainties in Missile Servo Control System

Real-time online Compensation the Nonlinearities and Uncerta...

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第27届中国控制与决策会议

作者： Zhang Kun feng Rao Da wei Zhu Jian bo Duan Li hua Luoyang Optoelectronic Technology Development Center

ISBN: (纸本)9781479970186

To restrain the influences of the nonlinearities and uncertainties in missile servo control systems,the active disturbance rejection control(ADRC) technique is introduced to real-time online estimate and compensate these *** to the problem of the difficulties in tuning ADRC parameters,an improved multi-objective genetic algorithm is adopted to solve the complete set of Pareto solutions for the control parameters,then a multi-criteria decision making algorithm based on utility function is chosen to determine the fittest *** a test platform using TMS320C28346 floating-point DSP as control core is built to verify the research *** experimental results show that the optimal ADRC parameter is appropriate for the servo control *** system tracks the input commands smoothly and reliably,and has good robustness and adaptability to nonlinearities and parameters *** the strategy for tuning the ADRC parameters is practical and feasible.

关键词： Nonlinearity and Uncertainty Active Disturbance Rejection Control multi-objective genetic algorithm

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Design of hybrid power generation systems based on multi criteria decision analysis

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SOLAR ENERGY 2014年 105卷 548-560页

作者： Alsayed, M. Cacciato, M. Scarcella, G. Scelba, G. An Najah Natl Univ Dept Energy Engn & Environm Nablus Israel Univ Catania Dept Elect Elect Engn & Comp Sci I-95125 Catania Italy

The optimal design of Hybrid Photo Voltaic-Wind Turbine (PV-WT) grid connected power generation systems (PGSs) is not an easy or straightforward task, as the best configuration depends on many different uncertain socio-economic and environment variables, design parameters, technical constraints and government and policy maker interests. In this context, this work deals with the optimal design of grid connected PV-WT PGSs, achieved by using a suitable procedure based on a multi Criteria Decision Analysis (MCDA) optimization approach. The method takes into consideration all the aforementioned issues, working toward optimizing amount of reduced emissions, total costs, and social acceptability, and providing a clear overview of the most suitable PV-WT design configurations. The proposed approach has been applied to different case studies and uncertainty analysis related to solar radiation, wind speed and load demand profiles variations has been carried out using a probabilistic approach;in addition, different criteria weights have been adopted for sensitivity analysis, where the importance of each design parameter has been assumed to be uncertain, and its effect on the final optimal design is investigated. Results confirm the effectiveness and flexibility of the proposed approach, which could be assumed as a powerful decision making tool in designing hybrid renewable energy grid connected PGSs. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Photovoltaic power generation systems Wind turbines Renewable energy multi criteria design analysis multi-objective genetic algorithm

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Optimal Test Points Selection Based on multi-objective genetic algorithm

Optimal Test Points Selection Based on Multi-objective Genet...

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IEEE Circuits and Systems International Conference on Testing and Diagnosis

作者： Zhang, Yong Chen, Xixiang Liu, Guanjun Qiu, Jing Yang, Shuming Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781424425860

A new approach to select an optimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The problem of optimal test points selection is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method efficiently and accurately rinds the optimal set of test points and is practical for large scale systems.

关键词： Test points selection system testing design for testability multi-objective genetic algorithm

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multi-objective fuzzy control of smart base isolated spatial structure

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2014年第3期14卷 547-556页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

Base isolation systems are used widely to reduce the dynamic responses of structures subjected to a seismic load. Recently, research has been conducted actively on smart base isolation systems that can effectively reduce the dynamic responses of isolated structures without accompanying increases in the base drifts. On the other hand, control performance of smart base isolation systems for spatial structures has not attracted significant attention. This study examined the dynamic response reduction capacity of a smart base isolation system for a spatial structure subjected to seismic excitation. MR dampers and low damping elastomeric bearings were used to compose a smart base isolation system, and its vibration control performance was compared with that of the optimally-designed, lead-rubber bearing (LRB) isolation system. A fuzzy controller was used to effectively control the spatial structure with a smart base isolation system. The dynamic responses of the spatial structure with an isolation system conflicted with the base drift. Therefore, these two responses were selected as the objective functions to apply a multi-objective genetic algorithm to optimize a fuzzy controller. The numerical simulation results showed that the smart base isolation system proposed in this study can reduce drastically the base drifts and seismic responses of the example spatial structure compared to the optimally designed LRB isolation system.

关键词： smart base isolation system spatial structure seismic response reduction fuzzy logic control multi-objective genetic algorithm MR damper lead-rubber bearing

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multi-objective Waste Load Allocation Model for Optimizing Waste Load Abatement and Inequality Among Waste Dischargers

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WATER AIR AND SOIL POLLUTION 2014年第3期225卷 1-17页

作者： Cho, Jae Heon Lee, Jong Ho Kwandong Univ Dept Hlth & Environm Kangnung 210701 Gangwon Do South Korea Cheongju Univ Dept Urban Planning Cheongju 360764 Chungbuk South Korea

In allocating the waste load of a river basin, the first priority is to achieve a given water quality goal for that river by utilizing several water quality management methods. Minimizing the waste load abatement cost within the river basin through appropriate, efficient water quality management is an important aspect of this process. In the past, it was common to concentrate on economic factors when constructing a waste load allocation (WLA) model. However, environmental resources (e. g., sub-basin area, population, wastewater flow, etc.) vary in each region of a river, and the fairness in the distribution of the treatment efforts among waste dischargersmust be considered. The WLA model in this study was constructed as a multi-objective optimization problem and was established to achieve the economic goal of minimizing waste load abatement and to consider the inequality among waste dischargers. Two types of inequality were introduced into the WLA model. The first type is the inequality in the waste load discharge regarding the environmental resources in each region was computed with the environmental resource-based Gini coefficient. The second type of inequality is the fairness in the distribution of the treatment efforts among waste dischargers. The suitability of this WLA model was verified with its application in a heavily polluted total maximum daily load subject river in South Korea. Furthermore, Pareto-optimal solutions drawn from the multi-objective genetic algorithm were analyzed to infer the least cost solution, the least inequality solution, and the compromise solutions and to verify critical pollution sources.

关键词： WLA Inequality ER-Gini multi-objective genetic algorithm Pareto-optimal solutions Critical pollution sources

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