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2nd International Conference on Advanced Engineering Materials and Technology (AEMT)

作者： Sun, Qixin Xi, Pingyuan Zhang, Renjian Huaihai Inst Technol Coll Mech Engn Lianyungang 222005 Peoples R China

ISBN: (纸本)9783037854471

Finite element analysis and fuzzy multi-objective optimization technique have been integrated to solve the die structure design of sheet metal forming by transforming fuzzy multi-objective problem into a normal optimization problem. A mathematical model of fuzzy optimization for bending die was established. A fuzzy goal set was constructed. The fuzziness of multi-objective functions and constraints were defined. The optimal solution and optimal constraint value of individual objective function in the feasible field were found using the genetic algorithms. An electronic part bending case shows that this approach is more effective and accurate than traditional finite element analysis method and the 'trial and error' procedure.

关键词： Finite element analysis fuzzy multi-objective optimization Bending die Genetic algorithm

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fuzzy multi-objective optimization of EMT based on the minimum average weighted deviation algorithm 8th

Fuzzy multi-objective optimization of EMT based on the minim...

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8th International Conference on Applied Energy (ICAE)

作者： Ma, Pengtao Han, Lijin Liu, Hui Zhang, Hui Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Natl Key Lab Vehicular Transmiss Beijing 100081 Peoples R China Collaborate Innovat Ctr Elect Vehicle Beijing Beijing 100081 Peoples R China

The optimal control of the vehicles with electro-mechanical transmission is a problem of multi-objective optimization. In this paper, based on the working characteristics of the engine, the battery pack and the motor/generators (MGs) as well as their interactions, a multi-objective optimization model with the optimal objective functions which include drivability, electric supply performance and fuel consumption is established. Using the relative membership degree formulas, the multi-objective optimization is transformed to fuzzy multi-objective optimization, and the problem of multi-objective optimization is converted to single-objective optimization with the corresponding weights of the objective functions gained by the minimum average weighted deviation algorithm. The forward model based on Matlab is implemented. Compared with the rule-based method, this method had enabled the drivability, the electric supply performance and the fuel consumption to improve by 29.8%, 38.1% and 11.8%, respectively. (C) 2017 The Authors. Published Ltd.

关键词： fuzzy multi-objective optimization electro-mechanical transmission minimum average weighted deviation algorithm

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Determining Human-Hepatitis C Virus Protein Interactions: A synergism of fuzzy multi-objective optimization and machine intelligent models

Next Research

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Next Research 2025年第1期2卷

作者： Sajal Mitra Arijit Chakraborty Mainak Bhattacharjee Debashis De Anindya J. Pal Department of Computer Science and Engineering Heritage Institute of Technology Chowbaga Road Anandapur Kolkata-700107 West Bengal India Bachelor of Computer Application Department The Heritage Academy Chowbaga Road Anandapur Kolkata-700107 West Bengal India Department of Biotechnology Heritage Institute of Technology Chowbaga Road Anandapur Kolkata-700107 West Bengal India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Haringhata-741249 Nadia West Bengal India University of Burdwan Rajbati Bardhaman 713104 West Bengal India

The cumulative exigent for treating Hepatitis C Virus (HCV) is significantly compounded by the substantial risk of cellular toxicity associated with current therapies, such as interferon-α and ribavirin. This scenario highlights the pressing necessity of confronting the challenges presented by HCV. The virus adversely affects millions of individuals worldwide, thereby necessitating the development of innovative and safer therapeutic alternatives, including repurposing of existing medications. Lately, research studies demonstrated the efficacy of machine-intelligent methods for examining host-pathogen Protein-Protein Interactions (PPI); however, they primarily lean on a single machine-intelligent strategy. The reliance on specific machine-intelligent methods may hinder understanding complex protein interactions and high mutation rates. The present study integrates four advanced techniques, machine learning, deep learning, ensemble learning, and fuzzy multi-objective optimization, to design the proposed model, namely multi-objective optimization Deep Machine Learning Average Probability-based Ensemble in Virology (MADHAV). The MADHAV model shows strong potential for advancing research in virology. The MADHAV model, with its robust classification accuracy of 68 %, outperforms current machine-learning algorithms and stacking techniques. It identifies 207 non-interacting human proteins that HCV may target with a high likelihood of ≥ 80 %. This reliable finding supported by gene ontology and KEGG pathway enrichment analyses. The predicted human proteins accurately mirror the host-pathogen PPI scenarios associated with eleven other lethal viruses, including coronavirus, emphasizing the potential for repurposing existing antivirals. The low binding site energy of -9.8 kcal/mol, comparable to traditional anti-HCV medications, supports repurposing antivirals like alcuronium, thrombin, prednisolone, and doxorubicin hydrochloride as potential anti-HCV treatments, pending clinic

关键词： Ensemble learning fuzzy multi-objective optimization Hepatitis-C virus Machine learning Protein-protein interaction

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A green train scheduling model and fuzzy multi-objective optimization algorithm

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APPLIED MATHEMATICAL MODELLING 2013年第4期37卷 2063-2073页

作者： Li, Xiang Wang, Dechun Li, Keping Gao, Ziyou Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

Train scheduling model is traditionally formulated to minimize the energy consumption for reducing the operation cost. As the European Union formulates the first carbon emission trading scheme in the world, it is necessary to extend the operation cost to include the expenses for buying/selling the carbon emission allowances. In this paper, we propose a multi-objective train scheduling model by minimizing the energy and carbon emission cost as well as the total passenger-time, and named it as green train scheduling model. For obtaining a non-dominated timetable which has equal satisfactory degree on both objectives, we apply a fuzzy multi-objective optimization algorithm to solve the model. Finally, we perform two numerical examples to illustrate the efficiency of the proposed model and solution methodology. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Train scheduling Energy consumption Carbon emission fuzzy multi-objective optimization

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fuzzy multi-objective optimization of EMT Based on the Minimum Average Weighted Deviation Algorithm

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Energy Procedia 2017年 105卷 2409-2414页

作者： Pengtao Ma Lijin Han Hui Liu Hui Zhang School of Mechanical Engineering Beijing Institute of Technology Beijing 100081 China National Key Lab of Vehicular Transmission Beijing 100081 China Collaborate Innovation center of Electric Vehicle of Beijing Beijing 100081 China

关键词： fuzzy multi-objective optimization electro-mechanical transmission minimum average weighted deviation algorithm

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A Novel Interactive Satisfying Method via Goal Programming with Alternative Tolerance for fuzzy multi-objective optimization

A Novel Interactive Satisfying Method via Goal Programming w...

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23rd Chinese Control and Decision Conference

作者： Hu, Chaofang Wang, Na Zong, Qun Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China Tianjin Polytech Univ Sch Elect Engn & Automat Tianjin 300160 Peoples R China

ISBN: (纸本)9781424487363

A novel interactive satisfying method via goal programming with alternative tolerance is proposed for fuzzy multi-objective optimization problem with progressive preference. According to decision maker, the new tolerances of all the objectives are generated to modify the membership functions or add the objective constraints through the auxiliary programming. The optimization models based on goal programming are presented to handle three types of fuzzy relations. The dissatisfying objectives are improved iteratively till the satisfying result is acquired. Numerical example shows power of the proposed method.

关键词： fuzzy multi-objective optimization Interactive Goal programming

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A Novel Interactive Satisfying Method via Goal Programming with Alternative Tolerance for fuzzy multi-objective optimization

A Novel Interactive Satisfying Method via Goal Programming w...

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2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Chaofang Hu Qun Zong Na Wang School of Electrical Engineering and Automation Tianjin University Tianjin 300072 China School of Electrical Engineering and Automation Tianjin Polytechnic University Tianjin 300160 Ch

关键词： fuzzy multi-objective optimization Interactive Goal programming

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An interactive method using genetic algorithm for multi-objective optimization problems modeled in fuzzy environment

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第3期38卷 1659-1667页

作者： Deep, Kusum Singh, Krishna Pratap Kansal, M. L. Mohan, C. Indian Inst Technol Dept Math Roorkee 247667 Uttrakhand India Ambala Coll Engn & Appl Res Ambala Haryana India

In this paper, an interactive approach based method is proposed for solving multi-objective optimization problems. The proposed method can be used to obtain those Pareto-optimal solutions of the mathematical models of linear as well as nonlinear multi-objective optimization problems modeled in fuzzy or crisp environment which reasonably meet users aspirations. In the proposed method the objectives are treated as fuzzy goals and the satisfaction of constraints is considered at different a-level sets of the fuzzy parameter used. Product operator is used to aggregate the membership functions of the objectives. To initiate the algorithm, the decision maker has to specify his(er) preferences for the desired values of the objectives in the form of reference levels in the membership space. In each iterative phase, a single objective nonlinear (usually nonconvex) optimization problem has to be solved. It is solved using real coded genetic algorithm, MI-LXPM. Based on its outcomes, the decision maker has the option to modify, if felt necessary, some or all of the reference levels in the membership function space before initiating the next iterative phase. The algorithm is stopped where user's aspirations are reasonably met. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： fuzzy multi-objective optimization Interactive methods Nonlinear programming

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multi-layer collaborative optimization method for solving fuzzy multi-objective integrated process planning and scheduling

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MEASUREMENT & CONTROL 2020年第9-10期53卷 1883-1901页

作者： Wen, Xiaoyu Lian, Xiaonan Wang, Kanghong Li, Hao Luo, Guofu Zhengzhou Univ Light Ind Henan Prov Key Lab Intelligent Mfg Mech Equipment 5 Dongfeng Rd Zhengzhou 450002 Henan Peoples R China

Research on integrated process planning and scheduling (IPPS) is of great significance to the improvement of the overall quality of machinery manufacturing system. In the actual manufacturing process, the manufacturing system is often accompanied by some unpredictable uncertain disturbance factors, for instance uncertain processing time of jobs and changes of due date, etc. These uncertain disturbance events will ultimately affect production efficiency and customer satisfaction. Consequently, this paper considers the multi-objective IPPS problem with uncertain processing time and uncertain due date. A multi-layer collaborative optimization (MLCO) method is designed for the fuzzy multi-objective IPPS (FMOIPPS) problem, including three layers. For the process planning layer, the basic genetic algorithm is used to provide various near optimal process plans for the process selection system. For the process selection layer, a multi-objective genetic algorithm (MOGA) is designed to optimize the process selection population. A sharing function method is introduced to maintain population diversity. An individual comprehensive evaluation method is introduced to evaluate non-dominated solutions. The crowded distance, fast non-dominated sorting and elite strategy based on NSGAII is adopted in the proposed MOGA. The external archive method is employed to preserve the non-dominated solutions generated during population evolution. For the scheduling layer, a MOGA with a boundary search strategy is proposed. The boundary search strategy is designed to improve the search ability of boundary solutions. Three optimization objectives are minimizing the spread of fuzzy makespan, minimizing fuzzy makespan and maximizing average customer satisfaction simultaneously. The target of scheduling layer is to make scheduling arrangements for the process information obtained by process selection layer. Through mutual cooperation among each layer, guide the overall optimization process, and final

关键词： Integrated process planning and scheduling fuzzy multi-objective optimization sharing function boundary search strategy

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A PSO-Based fuzzy Long-Term multi-objective optimization Approach for Placement and Parameter Setting of UPFC

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第4期39卷 2953-2963页

作者： Ghadimi, Noradin Afkousi-Paqaleh, Ali Emamhosseini, Ali Islamic Azad Univ Ardabil Branch Young Researchers Club Ardebil Iran Islamic Azad Univ Dept Elect Engn Yazd Branch Yazd Iran

This paper presents a Particle Swarm optimization (PSO)-based fuzzy multi-objective methodology for optimal locating and parameter setting of Unified Power Flow Controller (UPFC) in a power system for a long-term period. One of the profits obtained by UPFC placement in a transmission network is the reduction in total generation cost due to its ability to change the power flow pattern in the network. Considering this ability, UPFC can be also used to remove or at least mitigate the congestion in transmission networks. The other issue in a power system is voltage violation which could even render the optimal power flow problem infeasible to be solved. Voltage violation could be also mitigated by proper application of UPFC in a transmission system. These objectives are considered simultaneously in a unified objective function for the proposed optimization algorithm. At first, these objectives are fuzzified and designed to be comparable against each other and then they are integrated and introduced to a PSO Algorithm to find the solution which maximizes the value of integrated objective function in a 3-year planning horizon, considering the load growth. A power injection model is adopted for UPFC. Unlike the most previous works in this field the parameters of UPFC are set for each load level to avoid inconvenient rejection of more optimal solutions. IEEE Reliability Test System is used as an illustrative example to show the effectiveness of the proposed method.

关键词： Congestion management fuzzy multi-objective optimization Particle swarm optimization Unified power flow controller Voltage violation

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