检索结果-内蒙古大学图书馆

Design of efficient multiobjective binary PSO algorithms for solving multi-item capacitated lot-sizing problem

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第2期37卷 1723-1750页

作者： Ben Ammar, Hanen Ben Yahia, Wafa Ayadi, Omar Masmoudi, Faouzi Univ Sfax Natl Sch Engn Sfax Mech Dept Mech Modelling & Prod Res Lab Rte SoukraBP 1173 Sfax 3038 Tunisia Univ Gabes Natl Sch Engn Gabes Mech Dept Gabes Tunisia

In this paper, a multi-item capacitated lot-sizing problem with setup times and backlogging (MICLSP_SB) is addressed with respect to two conflicting objective functions. This nondeterministic polynomial time (NP)-hard problem consists of finding optimal production plans while minimizing, simultaneously, the total cost and the total inventory level. To effectively solve the considered problem, two new versions of multiobjective binary particle swarm optimization are designed. In the first proposed version "standard multiobjective particle swarm optimization" (S-MOBPSO) algorithm, two main contributions are introduced. First, a new particle encoding and decoding method is developed to treat the MICLSP_SB decision variables. Second, the overall violation minimization method and the constraint handling method are combined to effectively handle the problem constraints. To further enhance the overall performance, an improvement procedure that minimizes the capacity constraints violations is developed and embedded into the S-MOBPSO algorithm. This second version is named improved MOBPSO (I-MOBPSO). An illustrative example is presented to explain the application of the proposed algorithms. Five performance metrics are considered to measure and evaluate the effectiveness of the proposed algorithms. Experimental results demonstrate the efficiency of the S-MOBPSO and I-MOBPSO algorithms compared to each other as well as the basic MOBPSO and nondominated sorting genetic algorithm II.

关键词： binary particle swarm optimization lot-sizing problem multiobjective optimization nondominated sorting genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A Self-Organizing Evolutionary Method to Model and Optimize Correlated Multiresponse Metrics for Additive Manufacturing Processes

SMART AND SUSTAINABLE MANUFACTURING SYSTEMS

引用

SMART AND SUSTAINABLE MANUFACTURING SYSTEMS 2019年第2期3卷 190-214页

作者： Aljarrah, Osama Li, Jun Huang, Wenzhen Heryudono, Alfa Bi, Jing Univ Massachusetts Dartmouth Dept Mech Engn 285 Old Westport Rd Dartmouth MA 02747 USA Univ Massachusetts Dartmouth Dept Math 285 Old Westport Rd Dartmouth MA 02747 USA Dassault Syst Simulia Inc Machine Learning Dassault Syst 1301 Atwood Ave Johnston RI 02919 USA

The use of robust multiresponse constrained optimization techniques in which multiple-objective responses are involved is becoming a crucial part in additive manufacturing (AM) processes. Common and popular techniques, in most cases, rely on the assumption of independent responses. In practice, however, many of the desired quality characteristics can be correlated. In this work, we propose a technique based on three ingredients: hybrid self-organizing (HSO) method, desirability function (DF), and evolutionary algorithms to analyze, model, and optimize the multiple correlated responses for the fused deposition modeling (FDM) process, one of the most popular AM technologies. The multiobjective functions are formulated by employing the HSO method and DF, where structural integrity and process efficiency metrics are considered for the data-driven correlated multiresponse models. Subsequently, layer thickness, nozzle temperature, printing speed, and raster angles are taken as process parameters (decision variables). The operational settings and capabilities for the FDM machine are defined as boundary constraints. Different EA algorithms, the nondominated sorting genetic algorithm, and the multiobjective particle swarm optimization method, are then deployed to model the AM criteria accordingly to extract the Pareto-front curve for the correlated multiresponse functions. FDM experimental design and data collection for the proposed method are provided and used to validate our approach. This study sheds light on formulating robust and efficient data-driven modeling and optimizations for AM processes.

关键词： additive manufacturing self-organizing method evolutionary algorithms desirability function nondominated sorting genetic algorithm multiple-objective particle swarm optimization correlate multiple-response optimization

来源：评论

学校读者我要写书评

暂无评论

Wide-Load Range Multiobjective Efficiency Optimization Produces Closed-Form Control Solutions for Dual Active Bridge Converter

引用

IEEE TRANSACTIONS ON POWER ELECTRONICS 2021年第8期36卷 8612-8616页

作者： Zhou, Lu Gao, Yihan Ma, Hao Krein, Philip T. Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Univ Illinois Urbana Champaign Inst Haining 314400 Peoples R China Univ Illinois Urbana IL 61801 USA

This letter proposes a multiobjective efficiency optimization method for triple-phase-shift (TPS) control of isolated dual active bridge converters (DABs). A comprehensive optimization of inductor current stress, inductor rms current, and zero-voltage switching (ZVS) is presented to reduce conduction losses, magnetic losses, and switching losses. A genetic algorithm is used to search for optimal solutions of the DAB nonconvex optimization problem. Analysis of these solutions gives rise to closed-form expressions for optimum TPS control angles, valid over various power and voltage levels. These can be realized for real-time operation. A 3.3-kW experimental prototype is used to verify the proposed optimized modulation control.

关键词： Zero voltage switching Optimization Magnetic losses Inductors Bridge circuits Magnetic switching Magnetomechanical effects Current minimization dual active bridge converters (DABs) nondominated sorting genetic algorithm rms current triple-phase-shift (TPS) control zero-voltage switching (ZVS)

来源：评论

学校读者我要写书评

暂无评论

Hybrid Surrogate Model Based Multiobjective Optimization Design of Flux-Modulated Permanent Magnet Machine for Shaftless Pump-Jet Propulsor

引用

IEEE TRANSACTIONS ON MAGNETICS 2024年第12期60卷

作者： Qin, Qinghai Yu, Haitao Fang, Shuhua Zhang, Qiongfang Liu, Yulei Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

This article proposes a novel hybrid surrogate model (SM) based multiobjective optimization method to optimize flux-modulated permanent magnet (PM) machine (FPMM) for the shaftless pump-jet propulsor. The proposed optimization method combining radial basis function neural network (RBFNN), support vector regression (SVR), and nondominated sorting genetic algorithm-III (NSGA-III) to achieve high torque performance and low harmonics of the back electromotive force (EMF). The topology and operating principle of the FPMM are addressed. The design variables are divided into different levels based on sensitivity analysis and the optimization objectives are selected. A hybrid SM is established based on the data space at different levels. NSGA-III is applied to obtain the nondominance solutions and the final design point is selected. The electromagnetic performance of the initial and optimized schemes is compared by finite element analysis (FEA), which verifies the effectiveness and superiority of the proposed multiobjective optimization method.

关键词： Optimization Torque Topology Sensitivity analysis Rotors Accuracy Pumps Network topology Motors Correlation coefficient Flux-modulated permanent magnet (PM) machine multiobjective optimization nondominated sorting genetic algorithm radial basis function neural network (RBFNN) shaftless pump-jet propulsor support vector regression (SVR)

来源：评论

学校读者我要写书评

暂无评论

Hydrodynamic Optimization of Foreship Hull-Form Using Contrastive Optimization algorithms

引用

JOURNAL OF COASTAL RESEARCH 2021年第5期37卷 1063-1078页

作者： Yin, Xunbin Lu, Qingliang Lu, Yu Zou, Jin Wan, Lei Harbin Engn Univ Coll Shipbldg Engn Harbin 150001 Peoples R China Naval Res Acad Beijing 100161 Peoples R China Dalian Maritime Univ Naval Architecture & Ocean Engn Coll Dalian 116026 Peoples R China

In this study, a hydrodynamic optimization design of the foreship hull-form for the Series 60 ship is presented in terms of minimum wave-making resistance by using contrastive optimization algorithms. The partially parametric approach was developed to modify the original foreship hull-form. The wave-making resistance as an objective function was obtained by the Rankine source panel method with nonlinear free-surface boundary conditions in which the numerical computation results were validated against available experimental data and found to be in good agreement with the test. Different optimal design methods were proposed based on the minimum wave-making resistance: the nonlinear programming method (NLP), the nondominated sorting genetic algorithm (NSGA-II), and particle swarm optimization (PSO). Through the implementation and integration of the hull-form deformation module, the hydrodynamic module, and the optimization module the hydrodynamic optimization framework can be established subsequently. This module realizes the full automation of ship-shape optimization design and searches for the most efficient target through intelligent optimization algorithm. The hydrodynamic optimization applications for the Series 60 ship indicate that the wavemaking resistance is reduced distinctly at various Froude numbers by the contrastive optimization algorithms and each optimized foreship hull-form is smoothing. The present study demonstrates an effective and robust integrated approach for the hydrodynamic optimization of ship design.

关键词： nondominated sorting genetic algorithm nonlinear programming method particle swarm optimization Rankine source method wave making resistance

来源：评论

学校读者我要写书评

暂无评论

Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Kariminejad, Mandana Tormey, David Ryan, Caitriona O'Hara, Christopher Weinert, Albert Mcafee, Marion Atlantic Technol Univ Sligo Ctr Precis Engn Mat & Mfg Res PEM Ctr Ash Lane Sligo F91YW50 Ireland Atlantic Technol Univ Ctr Math Modelling & Intelligent Syst Hlth & Envir Ash Lane Sligo F91YW50 Ireland Univ Coll Dublin John Hume Inst Res Ctr Adv Mfg I Form Dublin Ireland Trinity Coll Dublin Sch Med TCD Biostat Unit Discipline Publ Hlth & Primary Care Dublin Ireland St James Hosp Wellcome HRB Clin Res Facil Dublin D08NHY1 Ireland

Minimising cycle time without inducing quality defects is a major challenge in injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of injection moulding, however existing methods have limitations, including the need for a large number of experiments within a pre-determined search space. Bayesian adaptive design of experiment (ADoE) is an iterative process where the results of the previous experiments are used to make an informed selection for the next design. In this study, an experimental ADoE approach based on Bayesian optimisation was developed for injection moulding using process and sensor data to optimise the quality and cycle time in real-time. A novel approach for the real-time characterisation of post-production shrinkage was introduced, utilising in-mould sensor data on temperature differential during part cooling. This characterisation approach was verified by post-production metrology results. A single and multi-objective optimisation of the cycle time and temperature differential (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta T$$\end{document}) in an injection moulded component is proposed. The multi-objective optimisation techniques, composite desirability function and nondominated sorting genetic algorithm (NSGA-II) using the Response Surface Methodology (RSM) model, are compared with the real-time novel ADoE approach. ADoE achieved almost a 50\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} reduction in the number of experiments required for the single optimisation of \documentclass[12pt]{minimal} \usepackage{amsmath}

关键词： Injection moulding Gaussian process Bayesian adaptive design of experiments Multi-objective optimisation nondominated sorting genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Optimization of Wideband Spiral Arrays

Multi-Objective Optimization of Wideband Spiral Arrays

引用

2010 IEEE International Symposium Antennas and Propagation/CNC-USNC/URSI Radio Science Meeting

作者： Bianchi, Davide Fontana, Nunzia Genovesi, Simone Monorchio, Agostino Vallecchi, Andrea Cerretelli, Matteo Linari, Mariano Gentili, Guido Biffi Univ Pisa Microwave & Radiat Lab Dept Informat Engn Via Caruso 16 I-56122 Pisa Italy Univ Florence Dept Elect & Telecommun LAM I-50139 Florence Italy

ISBN: (纸本)9781424449682

A challenging task in array design is to synthesize an array working at multiple frequencies within a large frequency band, with constraints imposed to the radiation pattern in terms of side lobe level (SLL). This kind of array synthesis requires a multi-objective approach since there is no single solution which can be considered the best at the multiple investigated frequencies for all the compulsory requirements. Therefore this class of problems, instead of a single one, is characterized by an ideal set of solutions, the Pareto front (PF), which comprises solutions over which no other solution of the population dominates. This set provides a range of solutions for which no one can simultaneously satisfy the required performance but represents a tradeoff of the design requirements. Multi-objective genetic algorithms (MOAs) can be a useful tool for determining the Pareto front. They perform similarly to conventional genetic algorithms but they search for the set of solution which belongs to the PF, rather than providing only a single optimal solution. In particular, we have adopted a multi-objective genetic algorithm, called Non-dominated sorting genetic algorithm (NSGA-II) to optimize the array factor of a Spiral Array.

关键词： Pareto front Pareto optimisation antenna arrays antenna radiation pattern antenna radiation patterns array design array synthesis broadband antennas genetic algorithms multifrequency antennas multiobjective genetic algorithms multiobjective optimization approach nondominated sorting genetic algorithm side lobe level sorting spiral antennas wideband spiral antenna arrays

来源：评论

学校读者我要写书评

暂无评论

Comparison of Tree Encoding Schemes for Biobjective Minimum Spanning Tree Problem

Comparison of Tree Encoding Schemes for Biobjective Minimum ...

引用

2nd IEEE International Conference on Information and Financial Engineering (ICIFE)

作者： Sanger, Amit Kumar Singh Agrawal, Alok Kumar ABV Indian Inst Informat Technol & Management Gwalior India

ISBN: (纸本)9781424469284

Minimum Spanning Trees (MST) problem is a classical problem in operation research and network design problem is an important application of it. Minimum Spanning Tree (MST) problem can be solved efficiently, but its Biobjective versions are NP hard. In this paper, we compare three tree encoding schemes using Biobjective evolutionary algorithm. Three different tree encoding methods in the evolutionary algorithms are being used to solve three different instances of Biobjective Minimum Spanning Tree problem;comparative study of the tree encoding schemes used is done on the basis of Pareto optimal front obtained. Our approach involves Biobjective Minimum Spanning Tree problem using nondominated sorting genetic algorithm II (NSGAII). We compare Edge Set encoding, Priifer encoding, Characteristic Vector encoding using evolutionary algorithm for Biobjective Minimum Spanning Tree, we find that edge sets encoding performs better than Priifer and Characteristic Vector for Biobjective Minimum Spanning Tree problem while we are solving Biobjective Minimum Spanning Tree problem using nondominated sorting genetic algorithm II (NSGAII).

关键词： Minimum Spanning Tree nondominated sorting genetic algorithm Biobjective optimization scenario Evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Reducing the Guard-band of a DDO-OFDM System by Multi-Objective Optimization

Reducing the Guard-band of a DDO-OFDM System by Multi-Object...

引用

Microwave and Optoelectronics Conference (IMOC), 2015 SBMO/IEEE MTT-S International

作者： Pereira, Esequiel V. Rocha, Helder R. O. Nunes, Reginaldo B. Segatto, Marcelo E. V. Silva, Jair A. L. Fed Univ Eespirito Santo Dept Elect Engn LABTEL Ave Fernando Ferrari BR-29075910 Vitoria ES Brazil CEUNES Comp & Elect Dept Sao Mateus Brazil

ISBN: (纸本)9781467394925

A Multi-objective optimization on the parameters of direct-detection optical orthogonal frequency division multiplexing ( DDO-OFDM) systems in short-range links is proposed. Based on genetic algorithms ( GA), the optimization process takes into account the influence of optical power in the guard-band reduction of such multicarrier systems with 4 and 16-QAM subcarrier mapping and for propagation in 40 km of standard single-mode fiber ( SSMF). Simulation results show that the parameters optimization is responsible for a gain of approximate to 2.5 dB [approximate to 1.8 dB] in the required optical power for a bit-error-rate of 10(-3), for DDO-OFDM system transmission over 40 km of uncompensated SSMF, with a guard band (B-G) equals to 50% of the signal bandwidth (B-w) and 16-QAM [ 4-QAM] subcarrier mapping. For a extremely reduced guard-band of only 1% of B-w, the gain reduced to approximate to 0.5 dB and approximate to 1.5 dB for 16-QAM and 4-QAM mapping, respectively.

关键词： Multi-objective optimization direct-detection orthogonal frequency division multiplexing nondominated sorting genetic algorithm spectral efficiency

来源：评论

学校读者我要写书评

暂无评论

Optimization of Turning Process Parameters using Multi-objective Evolutionary algorithm

Optimization of Turning Process Parameters using Multi-objec...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Datta, Rituparna Majumder, Anima Indian Inst Technol Dept Mech Engn Kanpur Genet Algorithms Lab KanGAL Kanpur 208016 Uttar Pradesh India Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781424481262

Machining parameters optimization is very crucial in any machining process. This research focuses on Multi-objective Evolutionary algorithm based optimization technique, to determine optimal cutting parameters (cutting speed, feed, and depth of cut) in turning operation. Two conflicting objectives (operation time and tool life) with three constraints, which depends on the turning parameters, are optimized using genetic algorithm (GAs). The Pareto-optimal front of the bi-objective problem is obtained using Non-dominated sorting genetic algorithm (NSGA-II). The extreme and intermediate points of Pareto optimal front is verified using Real coded genetic algorithm (RGA) as well as epsilon-constraint method. The performance of NSGA-II is found to be more effective and efficient as compared to micro-GA. Innovization study carried out to correlate cutting parameters with the aforementioned objective functions. The effect of cutting speed is found more as compared to feed rate and depth of cut.

关键词： Pareto optimal front Pareto optimisation cutting genetic algorithms machining parameters optimization machining process multiobjective evolutionary algorithm nondominated sorting genetic algorithm optimal cutting real coded genetic algorithm turning (machining) turning process parameters optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：