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MODENA: a multi-objective RNA inverse folding

Advances and applications in bioinformatics and chemistry : AABC 2011年 4卷 1-12页

作者： Akito Taneda Graduate School of Science and Technology Hirosaki University Hirosaki Japan.

Artificially synthesized RNA molecules have recently come under study since such molecules have a potential for creating a variety of novel functional molecules. When designing artificial RNA sequences, secondary structure should be taken into account since functions of noncoding RNAs strongly depend on their structure. RNA inverse folding is a methodology for computationally exploring the RNA sequences folding into a user-given target structure. In the present study, we developed a multi-objective genetic algorithm, MODENA (multi-objective DEsign of Nucleic Acids), for RNA inverse folding. MODENA explores the approximate set of weak Pareto optimal solutions in the objective function space of 2 objective functions, a structure stability score and structure similarity score. MODENA can simultaneously design multiple different RNA sequences at 1 run, whose lowest free energies range from a very stable value to a higher value near those of natural counterparts. MODENA and previous RNA inverse folding programs were benchmarked with 29 target structures taken from the Rfam database, and we found that MODENA can successfully design 23 RNA sequences folding into the target structures; this result is better than those of the other benchmarked RNA inverse folding programs. The multi-objective genetic algorithm gives a useful framework for a functional biomolecular design. Executable files of MODENA can be obtained at http://***/modena/.

关键词： RNA sequence design Rfam multi-objective genetic algorithm secondary structure

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Research on the Optimal Layout of High-strength Steel in the Transmission Tower

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Physics Procedia 2012年 33卷 619-625页

作者： W.EI. Chunming S.U Tingting M.A Bin Gong Jing School of Civil Engineering Northeast Dianli University Jilin China

In order to research on the arrangement way of high-strength steel in ultrahigh voltage transmission towers, the integrated structure and material multi-objective optimization model of ultrahigh voltage transmission towers was established, and the optimization model is solved by using fast non-dominated sorting genetic algorithm (NSGA-II). Sectional areas and materials of each bar were regarded as the design variables, the structural min-cost was considered as the objective of the economic optimization, and the min-displacement of the control point was regarded as the objective of structural optimization. Based on the software MATLAB, relevant optimization program was programmed to solve the optimization model. The results show, the optimal results can satisfy the structural requirements and reduce the cost of projects, making the arrangement way of high-strength steel way in ultrahigh voltage transmission towers more reasonable and more economical.

关键词： Transmission towers with high-strength steel Integrated structure and material optimization multi-objective optimization multi-objective genetic algorithm

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MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

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INFORMATION 2018年第9期9卷 233-233页

作者： Nascimento, Zuleika Sadok, Djamel Univ Fed Pernambuco UFPE Informat Ctr BR-50740560 Recife PE Brazil

Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers due to the necessity of understanding the composition of network traffics, which changes over time, to ensure the network Quality of Service (QoS). Among the different methods of network traffic classification, the payload-based one (DPI) is the most accurate, but presents some drawbacks, such as the inability of classifying encrypted data, the concerns regarding the users' privacy, the high computational costs, and ambiguity when multiple signatures might match. For that reason, machine learning methods have been proposed to overcome these issues. This work proposes a multi-objective Divide and Conquer (MODC) model for network traffic classification, by combining, into a hybrid model, supervised and unsupervised machine learning algorithms, based on the divide and conquer strategy. Additionally, it is a flexible model since it allows network administrators to choose between a set of parameters (pareto-optimal solutions), led by a multi-objective optimization process, by prioritizing flow or byte accuracies. Our method achieved 94.14% of average flow accuracy for the analyzed dataset, outperforming the six DPI-based tools investigated, including two commercial ones, and other machine learning-based methods.

关键词： network traffic classification machine learning hybrid model multi-objective genetic algorithm extreme learning machine growing hierarchical self-organizing map

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multi-objective OPTIMIZATION OF HOT BLAST STOVE HEAT EXCHANGER

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INMATEH-AGRICULTURAL ENGINEERING 2023年第1期69卷 245-259页

作者： Zhang, Ye Jiang, Qing Xu, Liangyuan Huang, Chao Gan, Huasheng Anhui Agr Univ Coll Engn Hefei Anhui Peoples R China Anhui Prov Engn Lab Intelligent Agr Machinery & Eq Hefei Anhui Peoples R China

Shell-and-tube heat exchangers are widely used in many research fields and industrial production processes, but little research has been conducted on the use of heat exchangers for drying crops. This study conducted a numerical simulation of the temperature, velocity, and pressure fields based on the shell-and-tube fluids of a heat exchanger in a biomass particle hot-blast stove. The correctness of the simulation results was verified by test data before simulation, and the mesh was verified to be irrelevant. The application of a multi-objective genetic algorithm in heat exchanger design and optimization was explored, considering five design variables, such as hot tube diameter, transverse pitch, longitudinal pitch, cold flow velocity, and hot flow velocity for optimization. The Nusselt number, friction factor, and comprehensive performance coefficient were used as objective functions for 2D and 3D response surface analysis. The final design variables P1=74.91 mm, P2=104.23 mm, P3=121.37 mm, P4=4.83 m/s, and P5=8.48 m/s were obtained to improve the comprehensive performance coefficient by 16.11%. The heat transfer performance was improved by 9.55% and the resistance performance was reduced by 15%.

关键词： Heat exchanger Hot-blast stove multi-objective genetic algorithm Numerical simulation Response surface method

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

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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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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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A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

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SENSORS 2020年第5期20卷 1474-1474页

作者： Chui, Kwok Tai Lytras, Miltiadis D. Liu, Ryan Wen Open Univ Hong Kong Sch Sci & Technol Dept Technol Hong Kong Peoples R China Deree Coll Sch Business & Econ Amer Coll Greece Athens 15342 Greece Effat Univ Effat Coll Engn POB 34689 Jeddah Saudi Arabia Wuhan Univ Technol Sch Nav Hubei Key Lab Inland Shipping Technol Wuhan 430063 Peoples R China

Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals.

关键词： at-risk driving deep support vector machine driver drowsiness driver stress multi-objective genetic algorithm multiple kernel learning

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Interior High Frequency Noise Analysis of Heavy Vehicle Cab and multi-objective Optimization with Statistical Energy Analysis Method

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FLUCTUATION AND NOISE LETTERS 2017年第2期16卷 1750017-1750017页

作者： Chen, Shuming Wang, Lianhui Song, Jiqang Wang, Dengfeng Chen, Jing Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China FAW CAR CO LTD Changchun 130011 Peoples R China

The interior sound pressure levels of a commercial vehicle cab at the driver's right ear position and head rest position are determined as evaluation indices of vehicle acoustic performances. A statistical energy analysis model of the commercial vehicle cab was created by using statistical energy analysis method. The simulated interior acoustic performance of the cab has a significant coincidence with the experimental results. A response surface model was presented to determine the relationship between sound package parameters and evaluation indices of the interior acoustic performance for the vehicle cab. A multi-objective optimization was performed by using NSGA II algorithm with weighting coefficient method. The presented method provides a new idea for the multi-objective optimization design of the acoustic performances in vehicle noise analysis and control field.

关键词： Statistical energy analysis vehicle cab high frequency noise response surface method multi-objective genetic algorithm

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Fractional order [PI] Controller and Smith-like Predictor Design for A Class of High Order Systems

Fractional order [PI] Controller and Smith-like Predictor De...

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第30届中国过程控制会议（CPCC 2019）

作者： Zhenlong Wu Jie Yuan Yuquan Chen Donghai Li YangQuan Chen State Key Lab of Power Systems Department of Energy and Power Engineering Tsinghua University School of Automation Southeast University Department of Automation University of Science and Technology of China Mechatronics Embedded Systems and Automation(MESA)Lab School of Engineering University of California

To handle the control difficulties caused by high-order dynamics, a control structure based on fractional order [proportional integral]（PI） controller and fractional order Smith-like predictor for a class of high order systems in the type of K/（Ts+1）~n is proposed in this *** tracking performance and disturbance rejection ability are analyzed based on the terminal value *** shows that the system output can converge to the set point without static error when set point changes and input disturbance *** on the multi-objective genetic algorithm（MO-GA）, simulations under different χ are carried out to analyze the influence on control performance and control *** results show that the control performance can be improved and the energy of the control signal can be reduced simultaneously when the order χ is chosen no more than *** can verify that the fractional order Smith-like predictor with χ<1 has an advantage over the integral order Smith-like predictor with χ=1.

关键词： Fractional order [PI] Controller fractional order Smith-like predictor high order systems multi-objective genetic algorithm

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