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A multi-objective approach for multi-material topology and shape optimization

ENGINEERING OPTIMIZATION 2019年第6期51卷 915-940页

作者： Queiroz Zuliani, Joao Batista Cohen, Miri Weiss Guimaraes, Frederico Gadelha Severiano Junior, Carlos Alberto Ctr Fed Educ Tecnol Minas Gerais Timoteo Brazil Braude Coll Engn Dept Software Engn Karmiel Israel Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil Inst Fed Minas Gerais Campus Sabara Sabara Brazil

An automated multi-material approach that integrates multi-objective Topology Optimization (TO) and multi-objective shape optimization is presented. A new ant colony optimization algorithm is presented and applied to solving the TO problem, estimating a trade-off set of initial topologies or distributions of material. The solutions found usually present irregular boundaries, which are not desirable in applications. Thus, shape parameterization of the internal boundaries of the design region, and subsequent shape optimization, is performed to improve the quality of the estimated Pareto-optimal solutions. The selection of solutions for shape optimization is done by using the PROMETHEE II decision-making method. The parameterization process involves identifying the boundaries of different materials and describing these boundaries by non-uniform rational B-spline curves. The proposed approach is applied to the optimization of a C-core magnetic actuator, with two objectives: the maximization of the attractive force on the armature and the minimization of the volume of permanent magnet material.

关键词： Topology optimization shape optimization multi-objective ant colony optimization multi-objective genetic algorithm PROMETHEE II

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An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 146卷 109-123页

作者： Hajimani, Elmira Ruano, M. G. Ruano, A. E. Univ Algarve Fac Sci & Technol Faro Portugal Univ Coimbra CISUC Coimbra Portugal Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal

objective: This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. Methods: For the design of a neural network classifier, a multi objective genetic algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Results: Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602 pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). (C) 2017 Published by Elsevier Ireland Ltd.

关键词： Neural networks Symmetry features multi-objective genetic algorithm Intelligent support systems Cerebral vascular Accident

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An intelligent control system with a multi-objective self-exploration process

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FUZZY SETS AND SYSTEMS 2004年第2期143卷 275-294页

作者： Chen, LH Chiang, CH Natl Cheng Kung Univ Dept Ind Management Sci Tainan 701 Taiwan

This paper proposes a novel approach based on artificial intelligence technologies (multi-objective Self-Exploration process based Intelligent Control System-mSEICS) for intelligent control systems. Not only can this system adapt to various environments, but it can also continually improve its performance. The mSEICS consists of four basic functions, controller, receptor, m-adaptor and advancer. A five-layer fuzzy neural network is applied to implement the controller. The receptor is used to evaluate the performance of system. The m-adaptor (multi-objective based adaptor) that comprises two elements, action explorer and rule generator, can generate a variety of new action sets in order to adapt to various environments. The Pareto optimality based multi-objective genetic algorithm is proposed to implement the action explorer to discover multiple action sets, and the rule generator is employed to transform the action set to fuzzy rules. In addition, the advancer consisting of action discoverer and rule generator is constructed to produce the novel action set to enhance the system efficiency. The parallel-simulated annealing approach is presented to realize the action discoverer. An application of the robotic path planning is applied to demonstrate the proposed model. The simulation results show that the mobile robot can reach the target successfully in various environments, and the proposed model is more efficient than the similar model. (C) 2003 Elsevier B.V. All rights reserved.

关键词： intelligent systems fuzzy neural network multi-objective genetic algorithm fuzzy rules simulated annealing robotic path planning

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A multi-objective scatter search for a bi-criteria no-wait flow shop scheduling problem

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ENGINEERING OPTIMIZATION 2008年第4期40卷 331-346页

作者： Rahimi-Vahed, A. R. Javadi, B. Rabbani, M. Tavakkoli-Moghaddam, R. Univ Tehran Dept Ind Engn Tehran 14174 Iran Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran

The flow shop problem as a typical manufacturing challenge has gained wide attention in academic fields. This article considers a bi-criteria no-wait flow shop scheduling problem (FSSP) in which weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a FSSP has been proved to be NP-hard in a strong sense, a new multi-objective scatter search (MOSS) is designed for finding the locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm (GA), i.e. SPEA-II. The computational results show that the proposed MOSS performs better than the above GA, especially for the large-sized problems.

关键词： bi-criteria flow shop scheduling problem no wait multi-objective scatter search multi-objective genetic algorithm

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multi-objective optimization and experiment of nylon cord rubber in expandable packer

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Petroleum Science 2021年第1期18卷 269-284页

作者： Yan-Wen Zhang Han-Xiang Wang Jia-Qi Che Ming-Chao Du Hong-Jie Zhang College of Mechanical and Electronic Engineering China University of Petroleum(East China)Qingdao 266580ShandongChina

Nylon cord rubber has the advantages of small residual deformation and is easy to lift and lower the tubing string in low-permeability oil and gas ***,it is associated with low-pressure resistance and poor sealing *** enhance the performance of nylon cord rubber,a three-dimensional numerical model of the nylon cord rubber was established and its accuracy experimentally *** Plackett-Burman test,the Steepest climbing test and the Response surface method were used to acquire the polynomial response surface model connecting structural parameters with bearing and sealing *** genetic algorithms,optimal structural parameters of nylon cord rubber were determined depending on field *** reliability of the optimized results was verified by laboratory *** was shown that after optimization,the bearing capacity of the expandable packer increased by 25%while the sealing performance increased by 66%.In addition,the bearing pressure was 70 MPa while the sealing pressure was 50 *** measurements effectively met the on-site requirements of high-pressure and fine fracturing in low-permeability oil and gas reservoirs.

关键词： Low-permeability reservoirs Nylon cord rubber Numerical simulation Response surface method multi-objective genetic algorithm Laboratory test

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Uncertain multi-objective optimal model of oilfield development planning and its algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2017年第5期8卷 769-779页

作者： Ji, Xiaoyu Yan, Sen Feng, Siyu Renmin Univ China Sch Business Beijing 100872 Peoples R China

In this paper, we discuss the formulation of the oilfield development plan in case of significant nondeterminacy in oilfield development. And the plan needs to ensure production and achieve minimum cost and maximum new recoverable reserves. The uncertain factors in oilfield development are analyzed in this paper, and we consider the uncertain nature of the stimulation effect of measures and new recoverable reserves per well and quantify them. On this basis, an uncertain multi-objective optimal model of oilfield development planning is constructed. The model aims to minimize the expectation of development cost and maximize the expectation of new recoverable reserves, and optimizes the workload of each measure under the constraints including the oil production and the resources limitation. Based on uncertainty theory, the model is transformed into a deterministic model. And a nondominated sorting genetic algorithm with elite strategy is developed to solve the model and get the Pareto solution set. Then the multi-attribute decision-making is applied to select the multiple development plans, which provides the basis for the decision-making of the oilfield development plan. Finally, a numerical example is given to verify the effectiveness of the model and algorithm, and their practical application values under the background of oilfield development planning.

关键词： Oilfield development planning Uncertain programming Uncertain multi-objective programming multi-objective genetic algorithm

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Modal Expansion Analysis, Inverse Design, and Experimental Verification of a Broadband High-Aperture Efficiency Circular Short Backfire Antenna Loaded With Anisotropic Impedance Surfaces

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第6期71卷 4783-4798页

作者： Bie, Mengyuan Peng, Manxin Jiang, Zhi Hao Werner, Douglas H. Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Penn State Univ Dept Elect Engn University Pk PA 16802 USA

In this article, a broadband highly efficient circular short backfire antenna (SBFA) loaded with cylindrical anisotropic impedance surfaces (AISs) is proposed. An efficient modal expansion method (MEM) is developed to facilitate the analysis of the AIS-loaded SBFA with a finite-sized flange, which considerably reduces the computational resources as compared to commercial full-wave solvers. By combining the MEM with a multiobjective genetic algorithm, the geometrical dimensions of the antenna and the dispersive properties of the AIS are optimized through an inverse design strategy. As a proof-of-concept example, an S-band AIS-loaded SBFA with a broad bandwidth and high aperture efficiency is demonstrated, showing a good agreement between measured results and simulation predictions. An aperture efficiency of greater than 83% is experimentally achieved over a broad S-11 < -10 dB bandwidth of 35.3%, with a peak aperture efficiency of about 100%. Moreover, the underlying operating principle of the antenna is revealed by the investigation of the main propagating modes in the SBFA. The extraordinary performance demonstrates that the proposed AIS-loaded SBFA represents a promising candidate for various wireless communication systems.

关键词： Anisotropic impedance surfaces (AISs) broadband high-aperture efficiency mode expansion method multi-objective genetic algorithm short backfire antenna (SBFA)

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Simulation-oriented model reuse in cyber-physical systems: A method based on constrained directed graph

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 91-110页

作者： Wenzheng Liu Heming Zhang Chao Tang Shuangfei Wu Hongguang Zhu National CIMS Engineering Research Center Tsinghua University Beijing 100084P.R.China Gridsum Holding Inc.Beijing 100086P.R.China Center of Intelligent Control and Telescience Tsinghua Shenzhen International Graduate School Shenzhen 518055 P.R.China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing 100084 P.R.China

Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of ***-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new sim-ulation application,which avoid repeated modeling and thus reduce the redevelopment of *** composition,one of the important methods,enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application *** this paper,a real-time model integration approach for global CPS modeling is proposed,which reuses devel-oped submodels by compositing submodel ***,a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse *** properties,including co-simulation distance between submodel nodes,reuse benefit and simulation performance of model nodes,are *** on the properties,the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the genetic Algo-rithm(GA).In the experiment,the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions,among which the Pareto optimal solutions are *** show that the effectiveness of the model integration method proposed in this paper is verified.

关键词： CPS model reuse model composition co-simulation distance multi-objective genetic algorithm

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An Acceleration Feedback-Based Active Control Method for High-Speed Elevator Horizontal Vibration

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第2期12卷 1943-1956页

作者： Zhao, Mengmeng Qin, Chengjin Tang, Rui Tao, Jianfeng Xu, Shuang Liu, Chengliang Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

PurposeDue to the influence of uneven guide rail, rope swing and unbalanced shaft wind pressure, the high-speed elevator car may experience violent horizontal vibration, which seriously affects the comfort and safety of elevator passengers. Hence, it is vital to suppress horizontal vibration for high-speed elevator car (HSEC).MethodsThis paper proposes a horizontal vibration active control method for high-speed elevators via acceleration feedback. First, based on passive vibration reduction, the active control is used to establish a four-degree of freedom (4-DOF) horizontal vibration model for elevator car. Then, we design an acceleration feedback-based controller for horizontal vibration response induced by guide rail excitation. The control objective functions in the multi-objective genetic algorithm are the system performance and control cost. The Pareto optimal solution set of the optimal controller parameters can be obtained only through one optimization *** use the comprehensive control simulation analysis through MATLAB/Simulink to validate the vibration suppression effect for the proposed active control strategy. For the system performance, by comparing the time domain responses of no controller and using controllers C1/C2/C3, the horizontal vibration acceleration is reduced to a certain extent. The offset of the high-speed elevator car high-speed elevator car frame relative to the center of the hoistway and relative displacements between the HSEC and the HSEC frame are also reduced in a certain proportion. The control cost is employed to evaluate the control force and represents the energy consumption to a certain extent. Combining system performance and control costs, we choose C2 among the 3 sets of controller parameters, the horizontal vibration acceleration of the HSEC can be reduced by about 80%, while the power consumption is between C2 and *** proposed optimal controller could effectively suppress elevator car horizont

关键词： High-speed elevator Vibration suppression Acceleration feedback active control multi-objective genetic algorithm

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Machine Learning-Based Optimization of a Mini-Channel Heatsink Geometry

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第9期48卷 12107-12124页

作者： Saeed, Muhammed Kalule, Ramanzani S. S. Berrouk, Abdallah S. S. Alshehhi, Mohamed Almatrafi, Eydhah Khalifa Univ Sci & Technol Mech Engn Dept POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Ctr Catalysis & Separat CeCas POB 127788 Abu Dhabi U Arab Emirates Abu Dhabi Maritime Acad POB 54477 Abu Dhabi U Arab Emirates King Abdulaziz Univ Mech Engn Dept POB 80200 Jeddah Saudi Arabia

The research on cooling electronics devices in workstations, servers, and other devices has become one of the rapidly growing technologies today, with the generation of high heat fluxes associated with component compactness and increased power consumption. Hence, the current study aims to enhance the thermohydraulic performance of a compact mini-channel heat removal system based on a machine learning-based optimization technique. The design variables considered for the optimization study are the channel's Reynolds number (Re), fin thickness (t(f)), fin spacing (s(f)), and fin height (h(f)). Initially, the performance of the heat sink geometry with different fin configurations is computed using 3D-RANS simulations. The generated data are then used to train six regression techniques in machine learning to propose the best approach capable of accurately predicting the heat transfer coefficient and pressure drop data. The selected machine learning model is then coupled with the multi-objective genetic algorithm to find the optimal heat sink geometry. It is found that the multilayered perceptron approach re-designed to a deep neural network model efficiently predicts both the heat transfer coefficient and the pressure drop from the available data. The overall performance of the optimized channel geometry is found up to 2.1 times improved than the best available channel configuration in the data pool. Further, the optimized channel geometry's heat transfer coefficient was 14% higher, and the corresponding pressure drop was five times lower.

关键词： Heatsinks Machine learning Neural networks Mini channels multi-objective genetic algorithm

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