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Research on a lightweight unmanned sightseeing vehicle frame based on multi-condition and multi-objective optimization

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ADVANCES IN MECHANICAL ENGINEERING 2022年第10期14卷

作者： Tang, Pei Yang, Ruiqi Ni, Xiaohua Zhao, Haojie Xu, Fei Yancheng Inst Technol Sch Automot Engn 1 Middle Hope Dadao Rd Yancheng 224051 Jiangsu Peoples R China

A design method for a lightweight unmanned sightseeing vehicle frame was proposed based on multi-condition and multi-objective optimization to improve the vehicle's range and reduce its production cost. First, a finite element model of the frame is established, and its static and dynamic characteristics are analyzed. Then, the wall thickness of the middle beam of the frame was selected as the design variable, and its sensitivity was analyzed. Sample points were generated from the design variables using the Latin hypercube sampling method, and the corresponding response values of the sample points were calculated. A Kriging approximation model was established using the sample points and response values and replaced the actual model for optimization. Finally, a multi-condition and multi-objective optimization mathematical model of the frame was established with the minimum mass, maximum first-order natural frequency, and the minimum stresses under full load bending and torsion conditions as the objectives. The multi-objective genetic algorithm was used for the lightweight design by comparing the fuzzy matter element and analytic hierarchy process methods to select the optimal design and to verify the rationality of the final design scheme. The results show that this method results in an optimized frame meeting the strength requirements under various working conditions and reducing the frame mass by 5.4%.

关键词： Frame multi-condition and multi-objective optimization Kriging approximation model multi-objective genetic algorithm fuzzy matter element method analytic hierarchy process lightweight design

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Artificial intelligence based modelling and multi-objective optimization of vinyl chloride monomer (VCM) plant to strike a balance between profit, energy utilization and environmental degradation

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JOURNAL OF THE INDIAN CHEMICAL SOCIETY 2022年第1期99卷

作者： Pal, Sucharita Chowdhury, Somnath Hens, Abhiram Lahiri, Sandip Kumar Natl Inst Technol Durgapur Chem Engn Dept Durgapur 713209 India Natl Inst Technol Durgapur Dept Chem Engn Durgapur 713209 West Bengal India

The current research focuses on the creation of a general technique that solves the key issue of any operational chemical plant, namely, how to strike a delicate balance between profit and environmental impact. As a case study, a commercial vinyl chloride monomer (VCM) production unit was used. This research produced a new modelling and optimization tool that commercial chemical plants can use to measure their environmental impact and strike a careful balance between profit and environmental damage. This paper demonstrates how to model commercial complex reactors using Aspen and ANN in an easy-to-use manner. The current study used a multiobjective hybrid ANN and genetic algorithm to find a delicate balance between profit and environmental damage. A case study of a commercial VCM manufacturing process demonstrates the efficacy of the proposed methodology. The suggested methodology creates optimal VCM reactor operating parameters, which can be used in commercial plants to increase profit. Furthermore, the suggested methodology creates a set of Pareto optimal solutions platform to acquire insight into the profit-environmental impact balance. These insights could be extremely beneficial to plant management in making educated decisions about plant operations.

关键词： Aspen simulation multi-objective genetic algorithm ANN Sensitivity analysis Pareto

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Modular Granular Neural Networks Optimization with multi-objective Hierarchical genetic algorithm for human recognition based on iris biometric

Modular Granular Neural Networks Optimization with Multi-Obj...

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IEEE Congress on Evolutionary Computation

作者： Sanchez, Daniela Melin, Patricia Castillo, Oscar Valdez, Fevrier Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781479904549;9781479904532

In this paper a new model of a multi-objective Hierarchical genetic algorithm (MOHGA) based on the Micro genetic algorithm (mu GA) approach for Modular Neural Networks (MNNs) optimization is proposed. The proposed method can divide the data automatically into granules or sub modules, and chooses which data are for the training and which are for the testing phase. The proposed multi-objective genetic algorithm is responsible for determining the number of granules or sub modules and the percentage of data for training that can allow to have better results. The proposed method was applied to human recognition and its applicability with good results is shown, although the proposed method can be used in other applications such as time series prediction and classification.

关键词： Modular Neural Networks Hierarchical genetic algorithms multi-objective genetic algorithm Micro genetic algorithm Granular computing

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A multi-objective genetic algorithm approach to rule mining for affective product design

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第8期39卷 7411-7419页

作者： Fung, K. Y. Kwong, C. K. Siu, K. W. M. Yu, K. M. Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Sch Design Kowloon Hong Kong Peoples R China

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm Affective product design Rule mining

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A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第2-3期45卷 115-125页

作者： Wang, Xiaojuan Gao, Liang Zhang, Chaoyong Li, Xinyu Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

In many real-world applications, processing times may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm based on immune and entropy principle to solve the multi-objective fuzzy FJSP. In this improved multi-objective algorithm, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.

关键词： fuzzy flexible job-shop scheduling multi-objective genetic algorithm Pareto-optimality immune and entropy principle

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Geometry design for maximizing output power of segmented skutterudite thermoelectric generator by evolutionary computation

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APPLIED ENERGY 2020年 274卷 115296-115296页

作者： Chen, Wei-Hsin Chiou, Yi-Bin Natl Cheng Kung Univ Dept Aeronaut & Astronaut Tainan 701 Taiwan Tunghai Univ Coll Engn Dept Chem & Mat Engn Taichung 407 Taiwan Natl Chin Yi Univ Technol Dept Mech Engn Taichung 411 Taiwan Natl Cheng Kung Univ Res Ctr Energy Technol & Strategy Tainan 701 Taiwan

This study focuses on the design of a segmented thermoelectric generator system to maximize the system output power. Skutterudite, (Sr, Ba, Yb)(y)Co4Sb12 with 9.1% In0.4Co4Sb12, is used as the n-type element, while DD0.59Fe2.7Co1.3Sb11.8Sn0.2 is used as the p-type one. They both are employed as the hot side segments. Alternatively, hydrothermal synthesized nanostructure thermoelectric material (Bi0.4Sb1.6Te3) is chosen as the cold side segments. To achieve the optimum design, a numerical method is developed, while the multi-objective genetic algorithm is adopted. The evolutionary computation processes during seeking the optimum combination of the segments are visualized, and it is found that four generations are enough for reaching the target. With the leg length of 3 mm, the optimum n-type and p-type cold side segment lengths are 0.5 mm and 0.78 mm, respectively. Compared to the equal-segmented thermoelectric couple, the optimized couple at a temperature difference of 400 K can increase the output power by 21.94% and its efficiency is 14.05% which is much higher than conventional thermoelectric generators. The theory of impedance matching does not apply to the segmented thermoelectric generator. The heat flux distribution in the couple is dependent on the temperature difference. Overall, the segmented elements with evolutionary computation design is a promising tool for intensifying thermoelectric generator performance.

关键词： Segmented elements Skutterudite Thermoelectric generators Optimization multi-objective genetic algorithm Evolutionary computation

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Thermo-mechanical Evaluation of Slurry-Sprayed multi-layered Coatings

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第11期45卷 9449-9470页

作者： Naseem, Muhammed Verma, Rajeev Kango, Saurabh Dr BR Ambedkar Natl Inst Technol Dept Ind & Prod Engn Jalandhar 144011 Punjab India Dr BR Ambedkar Natl Inst Technol Dept Mech Engn Jalandhar 144011 Punjab India

Thermal barrier coatings (TBCs) represent a relatively thin layer of ceramic with the favourable insulating properties, which are generally used to improve the temperature stability of the engineering component such as turbine blades. One of the prime prerequisites of TBCs is to determine the optimised coating configuration with the desired thermo-mechanical properties and enhanced service life. In a typical functionally graded coating structure, this could be achieved by having a trade-off between the thermal insulation and fatigue toughness offered by ceramic in top coat and metal towards the substrate, respectively. In this work, a computational method was used to analyse and optimise the parameters pertaining to thermo-mechanical evaluation of the slurry spray-coated (SST) mullite-nickel ASTM 1018 steel. The composite material properties have been predicted using the classical mean-field micromechanics model and rule of mixtures and the finite element simulation package ANSYS. Experimental validation has been performed to analyse the relative thermal and structural properties of the composite layers of the coatings using transient plane source (TSP)-based thermal constants analyser. The predicted and experimental results were further analysed and optimised for various process parameters using response surface optimisation and multi-objective genetic algorithm, which evaluated the optimum results considering the boundary conditions with a temperature reduction of nearly 306 degrees C. Further, the research results indicate that suitable thickness of the coating configuration of the slurry-sprayed mullite-nickel TBC system included coating thickness of 57.2 mu m, 147.1 mu m, and 143.9 mu m for bond, intermediate, and top coats, respectively. The material properties were found dependent on the coating composition across the FG structure, and the predicted, computed, and experimental results were in reasonable agreement.

关键词： Thermo-mechanical evaluation Slurry spray technique Mean-field micromechanics Rule of mixtures Finite element simulation Transient plane source Optimisation multi-objective genetic algorithm

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The Portfolio Model Based on Temporal Convolution Networks and the Empirical Research on Chinese Stock Market 2021

The Portfolio Model Based on Temporal Convolution Networks a...

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2021 5th International Conference on Computer Science and Artificial Intelligence

作者： Rui Zhang Zuoquan Zhang Marui Du Xiaomin Wang Beijing Jiaotong University China

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Robustness of Time-multiplexed Hyperthermia to Temperature Dependent Thermal Tissue Properties

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IEEE JOURNAL OF ELECTROMAGNETICS RF AND MICROWAVES IN MEDICINE AND BIOLOGY 2020年第2期4卷 126-132页

作者： Cappiello, Grazia Paulides, Margarethus M. Drizdal, Tomas O'Loughlin, Declan O'Halloran, Martin Glavin, Martin van Rhoon, Gerard Jones, Edward Natl Univ Ireland Coll Engn & Informat Discipline Elect & Elect Engn Galway H91 TK33 Ireland Erasmus MC Canc Inst Dept Radiat Oncol Sect Hyperthermia NL-3015 GD Rotterdam Netherlands Eindhoven Univ Technol Fac Elect Engn NL-5612 AZ Eindhoven Netherlands Czech Tech Univ Dept Biomed Technol Fac Biomed Engn Kladno 27201 Czech Republic Natl Univ Ireland Lambe Inst Translat Res Translat Med Device Lab Galway H91 TK33 Ireland

Microwave hyperthermia is a promising cancer treatment used in combination with radio- and chemotherapy. Typically, hyperthermia systems involve several antennas that transfer electromagnetic energy into the tissue. The principal need in hyperthermia treatment is to optimally focus the heating into the target while minimising heating in the surrounding healthy tissue. Patient-specific treatment planning is done to optimize the specific absorption rate and the resulting temperature distribution. Uncertainties associated with the thermal model used for temperature simulations represent an important challenge. Our previous work has demonstrated that the occurrence of hotspots can be reduced and target heating enhanced using time-multiplexed steering procedures. In this paper, the robustness of time-multiplexed hyperthermia against temperature dependent thermal tissue properties is investigated. Temperature simulations are used to predict the time-dependent heating achieved by multiple antenna phase and amplitude configurations that are generated by a multi-objective genetic algorithm and applied sequentially. The proposed strategy is compared with the heating obtained using one single heating setting obtained by particle swarm optimization as typically used in clinical hyperthermia. Thermal performance of the static and time-multiplexed methods are assessed by applying two thermal models, one that uses constant properties of blood perfusion and thermal conductivity of tumor, muscle and fat, and a second one that uses temperature dependent perfusion values. This study shows that time-multiplexed hyperthermia enhances target heating and limits the hotspot appearance regardless of the thermal model used in thermal simulations.

关键词： Hyperthermia Hyperthermia Treatment Planning multi-objective genetic algorithm Thermal Tissue Properties

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A Hybrid multi-objective genetic algorithm for Bandwidth multi-Coloring Problem

A Hybrid Multi-Objective Genetic Algorithm for Bandwidth Mul...

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12th International Conference on Hybrid Intelligent Systems (HIS)

作者： Bayindir, Ismail Ugur Mercan, Engin Korkmaz, Emin Erkan Yeditepe Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781467351157;9781467351164

Standard genetic algorithm (GA) yields poor performance on the Graph Coloring Problem (GCP) and its variants. This paper presents a multi-objective genetic algorithm (MOGA) for Bandwidth multi-Coloring Problem (BMCP). The problem is a generalization of GCP. In the proposed method, genetic operations are replaced with new ones which suit better to the structure of the problem. Performance of this MOGA framework is further boosted by hybridizing it with a Local Search (LS) algorithm. The aim of this hybrid approach is to increase the variety within the population through the genetic operations and to improve those individuals further by using LS. Several tests were conducted on a collection of benchmarks from GEOM series and promising results are obtained.

关键词： Graph Coloring Problem Bandwidth multi-Coloring Problem Hybrid genetic algorithm multi-objective genetic algorithm

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