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18th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Cea-Montufar, Cesar E. Merchan-Cruz, Emmanuel A. Ramirez-Gordillo, Javier Gutierrez-Mejia, Barbara M. Vergara-Hernandez, Erasto Nava-Vega, Adriana Inst Politecn Nacl Unidad Profes Interdisciplinaria Ingn Campus Hidalgo Mexico City 42162 Hidalgo Mexico Inst Politecn Nacl Secc Estudios Posgrad & Invest ESIME Azcapotzalco Azcapotzalco 02250 Mexico

ISBN: (纸本)9783030337490;9783030337483

This paper presents a path planning strategy for robotic manipulators based on genetic algorithms, dual quaternions and artificial potential field, designing a multi-objective function that allow trajectories be planned avoiding collisions in the workspace and singularity-free kinematic restrictions for manipulators as an optimization problem, satisfying position and orientation conditions. Its analysis is based on the problem of generating a trajectory followed by a sequence of coordinated movements capable of moving the manipulator to perform tasks in the workspace, the problem is not only generated these movements, but also implement strategies that define the path with tools that are easy to implement and avoid obstacles autonomously. Robot kinematics solved by dual quaternion can be used to combine translation with orientation on robotic manipulators in a systematic way, simplifying calculation operations compatible with conventional methods. The artificial potential field approach has been extended to collision avoidance for all manipulator links A genetic algorithm is used to solve the problem, which the fitness of the problem can be measured by a multi-objective function that involves the distance between the initial and desired position/orientation, minimum joint displacement, dual quaternion configuration, the use of attraction potential to the goal and a repulsion potential to the obstacles and its own links This method has been implemented in MatLab(C) for an ABB(C) IRB1600 robot. Collision avoidance demonstrations have been performed by simulating equipment and static objects in the robot's workspace.

关键词： Artificial potential field Path planning Dual quaternion multi-objective genetic algorithm Robot manipulator

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Configuration Analysis and Optimization of Collaborative Robots 3

Configuration Analysis and Optimization of Collaborative Rob...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Tian, Yong Wang, Hongguang Pan, Xinan Hu, Mingwei Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Liaoning Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781538662434

The configuration of collaborative robots is analyzed and optimized based on the Kriging model. First, through analyzing configurations of the existing collaborative robots, it can be seen that the offset is an important factor affecting the configuration, which is divided into S type and V type by the influence on the range of motion of the joint. Then, based on the Kriging model, the multi-objective optimization function is established, which takes the length and offset of each link as parameter variables and regards the global performance index, the workspace volume index and the workspace dexterity performance index as optimization objective. And the influence of the link and offset parameters of the robot on the robot performance index is analyzed. In the end, the pareto optimal solution set and the optimization configurations are obtained using multi-objective genetic algorithm, and the performance is improved, the solution provides theoretical basis for the configuration design of collaborative robots.

关键词： collaborative robots model offset multi-objective genetic algorithm optimization

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Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2018年第Jan.期67卷 147-156页

作者： Mustafa, Ahmed Heppenstall, Alison Omrani, Hichem Saadi, Ismail Cools, Mario Teller, Jacques Univ Liege Urban & Environm Engn Dept LEMA Liege Belgium Purdue Univ Dept Comp Sci 305 N Univ St W Lafayette IN 47907 USA Univ Leeds Sch Geog Leeds W Yorkshire England LISER Urban Dev & Mobil Dept Luxembourg Luxembourg Univ Liege Allee Decouverte 9Quartier Polytech 1 B-4000 Liege Belgium

This paper presents a model to simulate built-up expansion and densification based on a combination of a non ordered multinomial logistic regression (MLR) and cellular automata (CA). 'The probability for built-up development is assessed based on (i) a set of built-up development causative factors and (ii) the land-use of neighboring cells. The model considers four built-up classes: non built-up, low-density, medium-density and high density built-up. Unlike the most commonly used built-up/urban models which simulate built-up expansion, our approach considers expansion and the potential for densification within already built-up areas when their present density allows it. The model is built, calibrated, and validated for Wallonia region (Belgium) using cadastral data. Three 100 x 100 m raster-based built-up maps for 1990, 2000, and 2010 are developed to define one calibration interval (1990-2000) and one validation interval (2000 2010). The causative factors are calibrated using MLR whereas the CA neighboring effects are calibrated based on a multi-objective genetic algoritlun. The calibrated model is applied to simulate the built-up pattern in 2010. The simulated map in 2010 is used to evaluate the model's performance against the actual 2010 map by means of fuzzy set theory. According to the findings, land-use policy, slope, and distance to roads are the most important determinants of the expansion process. The densification process is mainly driven by zoning, slope, distance to different roads and richness index. The results also show that the densification generally occurs where there are dense neighbors whereas areas with lower densities retain their densities over time.

关键词： Built-up density Cellular automata multinomial logistic regression multi-objective genetic algorithm

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A system-of-systems meta-architecting approach for seru production system design

A system-of-systems meta-architecting approach for seru prod...

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IEEE International Conference on System of Systems Engineering (SoSE)

作者： Samuel Vanfossan Cihan H. Dagli Benjamin Kwasa Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla USA

ISBN: (数字)9781728180502

ISBN: (纸本)9781728180519

Sponsored by expedient technologic innovation, consumers frequently expect manufacturer offerings to exhibit extensive product variety and regular product advancement. These expectations have rendered many traditional production practices less applicable. Chiefly impacted is the notion of mass produced, low-variety artifacts via massive assembly lines. These operations have difficulty meeting the high-customization, short life-cycle requirements imposed by contemporary demand. Many industries and organizations have begun the transformation from these rigid assembly mechanisms to a more versatile, cellular production strategy known as seru production. To facilitate this transition, methods are needed to aid manufacturers in appropriately selecting and arranging seru system components, a critical step in seru system design. Herein, a generalized model is proposed utilizing a system-of-systems architecting approach to determine the component assembly best suiting the needs of the manufacturing entity. Candidate architectures are generated and evaluated using a multi-objective genetic algorithm from which a preferred alternative is selected through a fuzzy inference system. Directing this genetic algorithm, domain-independent objectives are presented, maintaining applications to most seru production design scenarios. The proposed method is then applied to a camera production example, culminating in the identification of a well-performing architecture. The presented method should find increased use as organizations further adopt this flexible production methodology.

关键词： Seru production system meta-architecture generation multi-objective genetic algorithm fuzzy inference system system-of-systems architecting fuzzy inference system Product Design component assemble multi-objective evolutionary algorithm Assembly Lines Production systems

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MOGA-Based Structural Design Method for Diagrid Structural Control System Subjected to Wind and Earthquake Loads

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2018年第5期18卷 1598-1606页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Architectural & Civil Engn Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

An integrated optimal structural design method for a diagrid structure and control device was developed. A multi-objective genetic algorithm was used and a 60-story diagrid building structure was developed as an example structure. Artificial wind and earthquake loads were generated to assess the wind-induced and seismic responses. A smart tuned mass damper (TMD) was used as a structural control system and an MR (magnetorheological) damper was employed to develop a smart TMD (STMD). The multi-objective genetic algorithm used five objectives including a reduction of the dynamic responses, additional stiffness and damping, mass of STMD, capacity of the MR damper for the integrated optimization of a diagrid structure and a STMD. From the proposed method, integrated optimal designs for the diagrid structure and STMD were obtained. The numerical simulation also showed that the STMD provided good control performance for reducing the wind-induced and seismic responses of a tall diagrid building structure.

关键词： Integrated structural optimal design Diagrid structural system Smart tuned mass damper multi-objective genetic algorithm Vibration control Wind excitation Earthquake load

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Application of entransy-dissipation-based thermal resistance for performance optimization of spiral-wound heat exchanger

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年 116卷 743-750页

作者： Wang, Simin Jian, Guanping Wang, Jiarui Sun, Lijuan Wen, Jian Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

The effects of geometrical parameters on thermal resistance based on entransy dissipation caused by heat transfer (R-ht) and fluid friction (R-ff) of spiral-wound heat exchanger (SWHE) were studied by numerical method. The simulation results show that all geometrical parameters (spiral angle, external diameter, layer pitch, tube pitch) are negatively correlated with R-ff because of flow pattern transition caused by the variation of geometrical parameters and the changing of effective flow area which would cause the decrease of entransy dissipation related to fluid friction. For the entransy dissipation due to the heat transfer, the increase of layer pitch are positive to it while both the tube pitch and external tube diameter are negative to R-ht, and with the increase of the spiral angle, the R-ht decrease at first and then increase. What is more, the MOGA optimization of SWHE was carried out based on different types of objective functions. Compared with the traditional objective functions (minimize Delta P and maximize K), R-ff and R-ht obtained from minimizing the entransy-dissipation-based thermal resistance reduce by an average of 90.51% and 34.13%, respectively. Compared with original structure, the comprehensive performance evaluation factor (Nu/f/(1/3)) of traditional optimal results is improved by an average of 41.02%, while that of optimal structures obtained from entransy theory is strengthened by an average of 76.64%. The results demonstrate that the objective functions of minimizing the entransy-dissipation-based thermal resistance are better than that of traditional objective functions for optimization of spiral wound heat exchanger. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Spiral-wound heat exchanger Entransy theory Entransy-dissipation-based thermal resistance multi-objective genetic algorithm Optimization

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HIV–TB co-infection treatment control using multi-objective optimized sliding mode

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Informatics in Medicine Unlocked 2020年 19卷

作者： Lakmesari, S. Hadipour Mahmoodabadi, M.J. Hadipour, S. Department of Mechanical Engineering Sirjan University of Technology Sirjan Iran Department of Infectious Diseases School of Medicine Guilan University of Medical Sciences Rasht Iran

Mycobacterium tuberculosis (MTB) and Human immunodeficiency virus (HIV) are two causes of infection which threaten human health over recent decades. There is abundant evidence indicating that these diseases are related to each other. People living with HIV have a 30-fold increase in tuberculosis infection compared to healthy people. In recent years, medical researchers and other specialists including engineers and basic scientists have tried to propose treatments for dangerous diseases by means of mathematical simulation. In this paper, a mathematical model of the HIV-TB co-infection individuals is regarded in order to represent different characteristics of such people. In the next step, a sliding mode controller is implemented on first and second HIV treatment rates to minimize the Total Burden resulted from the co-infection. Moreover, the multi-objective genetic algorithm (MOGA) is used to optimize the sliding mode controller coefficients. The MOGA is accurate and resilient and its Pareto front has a reasonable variety. In this method, designers acceptably use defeated points with more than one objective function and are also able to choose appropriate solutions according to their preferences. The results suggest that disease deaths, new infections, and immune reconstitution inflammatory syndrome cases play an important role in the problem. © 2020 The Authors

关键词： HIV-TB co-Infection Human immunodeficiency virus multi-objective genetic algorithm Sliding mode control Tuberculosis

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multi-objective demand fulfillment problem for solar cell industry

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 688-694页

作者： Zheng, Jia-Nian Chien, Chen -Fu Wu, Jei-Zheng Asia Univ Dept Business Adm 500 Lioufeng Rd Taichung 41354 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Soochow Univ Dept Business Adm 56 Sect 1Kuei Yang St Taipei 10048 Taiwan

With the growth of green energy market, there are many companies with vertical integration and outsourcing process. Their business models in the solar cell industry cause different influence on the business objectives including revenue, profit, and gross margin. The sales managers are hard to generate fast and reliable ATP (Available-to-promise) which covered all business objectives. Under the various products types with limited quantity, the loading of factory is uncertain and the quantity of order becomes huger. This study formulates multi-objective demand fulfillment problem in solar cell industry and applies a multi-objective genetic algorithm (moGA) to optimize the ATP. The model trade-offs objectives with by Pareto approach without sacrificing other benefits. The multi-population strategy used for stability of solutions. Results of empirical study show that the proposed moGA can improve gross margin while maintaining the desired revenue and profit. With effective solutions and reasonable computing time, the decision support system with proposed moGA has become a regular communication bridge between sales and production departments in the case company.

关键词： Demand fulfillment Solar cell multi-objective genetic algorithm Decision support system

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Pareto design of Load Frequency Control for interconnected power systems based on multi-objective uniform diversity genetic algorithm (MUGA)

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年第0期80卷 333-346页

作者： Nikmanesh, E. Hariri, O. Shams, H. Fasihozaman, M. Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran Iran Univ Guilan Dept Mech Engn Rasht Iran

In this study, it is tried to employ a state-of-the-art multi-objective uniform-diversity genetic algorithm (MUGA) for Pareto optimization of PI/PID controllers in Load Frequency Control (LFC) of power systems. At first, multi objective optimization of a linear non-reheat two-area interconnected power system is conducted with respect to three conflicting objective functions. Gains of PI and PID controllers are considered as design variables while the objective functions are Integral Time multiply Absolute Error (ITAE), minimum damping ratio of dominant eigenvalues, and settling times in frequency and tie-line power deviations. To illustrate superiority of MUGA in finding optimum values of the deign variables, the proposed designs by MUGA are compared with those proposed by single and multi-objective optimization methods such as BFOA, hBFOA-PSO, and NSGA-II;the results indicate there is a noticeable improvement in response of the system. Further, robustness of the proposed designs is demonstrated by varying the system parameters from their nominal values and monitoring sensitivity of the system response to the variations. At the end, to take nonlinearities and physical constraints into account and to evaluate performance of MUGA in more complex system, a three unequal area hydro thermal system with generation rate constraints is considered. (c) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-area power systems Load Frequency Control (LFC) PID controller Generation rate constraint (GRC) Pareto optimization multi-objective genetic algorithm

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The multi-objective Optimization Model of Flue Aimed Temperature of Coke Oven

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2018年第8期51卷 683-694页

作者： Tian, Zhongda Li, Shujiang Wang, Yanhong Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China

At present, the flue aimed temperature in coke oven production is mainly determined by artificial experience, which leads to the lack of theoretical guidance in the control process, and the parameters are difficult to adjust in real time. In this paper, the optimization model of the flue aimed temperature of coke oven is established. The correlation model between coke quality, coke yield, coking energy consumption and the flue aimed temperature is analyzed. Firstly, the factors affecting coke quality, coke yield and coking energy consumption are determined by mechanism analysis. Secondly, an improved grey relational analysis method is proposed to quantitatively analyze the correlation between coke quality, coke yield, coking energy consumption and their influencing factors, so as to determine the input of multi-objective optimization model. Because the neural network has a strong nonlinear approximation effect, the RBF neural network is introduced to establish a prediction model of coke quality, coke yield and coking energy consumption. Then, maximizing coke yield and minimizing coking energy consumption are chosen as the optimization object, the coke quality and production boundary are chosen as constraints, and the local optimization targets as decision variables, whereby the multi-objective optimization model is designed. The multi-objective genetic algorithm is applied to solve this multi-objective optimization problem. The optimal value of the flue aimed temperature of coke oven is obtained. Finally, the practical running results show that the proposed multi-objective optimization model has good applicability.

关键词： Coke Oven multi-objective genetic algorithm Flue Aimed Temperature Improved Grey Relational Analysis RBF Neural Network

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