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IEEE 15th International Conference of System of Systems Engineering (SoSE)

作者： Vanfossan, Samuel Dagli, Cihan H. Kwasa, Benjamin Missouri Univ Sci & Technol Engn Management & Syst Engn Rolla MO 65409 USA

ISBN: (纸本)9781728180502

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-lifecycle 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

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A growing complex network design method with an adaptive multi-objective genetic algorithm and an inner link restructuring method

A growing complex network design method with an adaptive Mul...

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Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE)

作者： Mizuno, Haruki Okamoto, Takashi Koakutsu, Seiichi Hirata, Hironori Chiba Univ Grad Sch Engn Chiba Japan

ISBN: (纸本)9784907764401

Many systems that can be modeled using network structures appear in various fields such as informatics, social science, economics, ecology, biology, and engineering. If these systems can be modeled as complex network systems, a complex network design method that finds a desired network structure can become one of strong tools in large-scale system designs. Conventional complex network design methods generally tackle static networks. On the other hand, a growing complex network design method has been proposed. In this method, the network is grown with determining the link structure of the newly-added node in order to obtain a desired network. In this design method, it is expected that characteristics of the optimization problem solved in the algorithm vary along with growth of the network. However, parameters of the optimization method are fixed;hence, its search process may be inefficient. In the design method, connection targets of a new node are only determined, and the structure of the existing network is not changed. Therefore, if the initial network has a large number of nodes, the desired network may not be obtained by using the design method. In this study, we propose to introduce an inner link restructuring method and an adaptive MOGA, that is, an adaptive SPEA2 into the growing complex network design method to deal with foregoing issues. Then, we confirm the effectiveness of the proposed method through numerical experiments.

关键词： Complex Network multi-objective Optimization Growing Network Network Design multi-objective genetic algorithm

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Cost-effective retrofits of power grids based on critical cascading failure scenarios identified by multi-group non-dominated sorting genetic algorithm

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2020年 49卷

作者： Choi, Eujeong Song, Junho Korea Atom Energy Res Inst Seoul South Korea Seoul Natl Univ Dept Civil & Environm Engn Seoul South Korea

Power supply network is one of the most critical infrastructure networks of urban communities, but prone to the risk of cascading failures. In efforts toward disaster risk reduction of urban communities, it is thus important to identify critical cascading failure scenarios of the power supply networks and prepare effective countermeasures based on the identified scenarios. While addressing these issues, previous research efforts focused on cascading failure scenarios induced by a single component although those induced by multiple components may occur under natural or man-made disaster events. A major challenge in this problem is high computational cost required for simulating cascading failure scenarios and solving large-size optimization problems. This paper first presents an effective method to identify multi-component failure combinations entailing critical cascading failures in the network by using the overload cascading model, the multi-group non-dominated sorting genetic algorithm (MG-NSGA), and the concept of critical zone. Based on the identified critical scenarios, cost-effective retrofit combinations against the risk of cascading failures are also identified using the proposed 'elite set updating' method. The proposed disaster risk reduction decision-making methods are demonstrated and tested by numerical examples of two complex power supply networks.

关键词： Power supply network Cascading failure scenarios Overload cascading failure multi-objective genetic algorithm Retrofit optimization

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Optimised Decision Making of Transportation Infrastructure Asset Management

Optimised Decision Making of Transportation Infrastructure A...

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世界交通运输工程技术论坛（WTC2021）

作者： Jiarong Li Chang'an University Department of Traffic Engineering

After more than 30 years of rapid development and construction,the transportation infrastructure achievements in China have attracted worldwide *** the other hand,the demand for Transportation Infrastructure Asset Management(TIAM)is growing quickly and strongly,and the rapid,safe and high-density traffic development trends have placed new demands on transportation infrastructure construction and *** the durable period of the pavement,it is still necessary to continue investing a large amount of funds to maintain and rebuild the pavement to keep their good performance,but the funds are not always available,so how to allocate the maintenance funds reasonably and keep the pavement in a good condition at the same time has become a hotpot,there are many decision-making methods to deal with it,but all of them have some deficiencies,this paper utilizes the inherent parallel mechanism and the characteristics of global optimization of genetic algorithm(GA),proposes a multi-objective genetic algorithm(MOGA)to simulate the problem of TIAM as a biological evolution problem,and by comparing the fitness of each generation of individuals to generate a new generation,in subsequent iterations,the generation is constantly replaced with a high fitness alternative to a low fitness value,so that after several generations,the best can be *** optimization result of a case study shows that the application of this algorithm in TIAM is successful and effective.

关键词： Transportation infrastructure asset management Optimised decision-making multi-objective genetic algorithm Large scale road network

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A multiobjective genetic algorithm for Analysis, Design and Optimization of Antipodal Vivaldi Antennas 9

A Multiobjective Genetic Algorithm for Analysis, Design and ...

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IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC)

作者： Antonio Pumallica-Paro, Marco Luis Arizaca-Cusicuna, Jorge Clemente-Arenas, Mark Univ Nacl San Antonio Abad Cusco Escuela Profes Ingn Elect Cuzco Peru Univ Nacl Ingn INICTEL UNI Inst Nacl Invest & Capacitac Telecomunicac DIDT Grp Radio Frecuencia Microondas & Opt G RFMO Av San Luis 1771 Lima 41 Peru

ISBN: (纸本)9781728105666

In the this contribution, a multi-objective genetic algorithm (MOGA) was implemented to analyze, design and optimize several Antipodal Vivaldi Antennas (AVA) that are being used on Ground Penetration Radars (GPR) applications. This algorithm was implemented and used as an optimization tool in a commercial available software. Directivity, lower cutoff frequency (LCF), bandwidth and cross-polarization were the objectives to be optimized. For the use of the MOGA, the 3D model of the antenna is built in the simulator. Some of its parameters are defined as variables, as the dimensions and shape of the antenna (Individual) depend on the value that its parameters (Genes) take. This work proves to be simple and time saving tool to optimize parameters in a geometry changing system as GPR.

关键词： multi-objective genetic algorithm Antipodal Vivaldi Antennas Ground Penetrating Radar

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An improved procedure to implement NSGA-III in coordinate waste management for urban agglomeration

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WASTE MANAGEMENT & RESEARCH 2019年第11期37卷 1161-1169页

作者： Qu, Lili Zhou, Zhihao Zhang, Tianzhu Zhang, Huanan Shi, Lei Tsinghua Univ Sch Environm State Key Joint Lab Environm Simulat & Pollut Con Beijing Peoples R China Columbia Univ Dept Earth & Environm Engn New York NY 10027 USA

With the growth of urbanization in countries globally, large cities have often formed clusters of urban agglomerations in metropolitan areas. The coordinated management of regional solid waste produced by such urban agglomeration poses a typical high-dimensional, multi-objective optimization issue. This paper aims to introduce a procedure to implement the third-generation genetic algorithm (NSGA-III), an established multi-objective genetic algorithm based on non-dominated sorting mechanisms, for the purpose of evaluating environmental and economic benefits simultaneously while seeking the optimal solutions for coordinated management among multiple recycling centres. In this study, two series of scenarios were abstracted from scrap tire recycling, representing linear calculation and nonlinear calculation cases separately. Several improvements were made to the originally published NSGA-III procedure that solve the problem of non-convergence for hypervolumes of the output. Through comparisons of calculation results, an improved procedure is suggested and shown to have improved performance.

关键词： Urban agglomeration waste management multi-objective genetic algorithm NSGA-III Jing-Jin-Ji

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multi-objective optimization of wavy microchannel heat sinks with symmetric configurations generated by interpolation curves

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Results in Engineering 2025年

作者： Zhen Li Haitao Han Xunqing Huang Jiarun Lou Qiancan He Haoran Zheng Xiangyao Xue Shuai Shao Changchun Institute of Optics Fine Mechanics and Physics (CIOMP) Chinese Academy of Sciences Changchun 130033 China University of Chinese Academy of Sciences Beijing 100039 China School of Mechanical Engineering Guizhou Institute of Technology Guiyang 550003 China Institute of laser manufacturing Henan Academy of Sciences Zhengzhou 450000 China

Microchannel heat sinks (MCHS) with symmetric configurations enhance fluid flow disturbances through expansion-contraction sections, improving heat transfer performance. This paper presents an optimal design for symmetrically curved MCHS generated by using interpolation curves. The multi-objective genetic algorithm based on decomposition (MOEA/D) is used to achieve the optimization of three design variables, and pumping power and thermal resistance are considered as the objective functions. Partial correlation analysis reveals that the design variables W 1 and W 3 describing the width of microchannels have the greatest influence on the two objective functions with different trends. The technique for order of preference by similarity to ideal solution (TOPSIS) is used to select the optimal compromise solution from the Pareto-optimal solutions. The channel first widens and then narrows in each unit of the TOPSIS solution and the solution with the minimum consumption of pumping power and maximum thermal resistance, which is opposite to the solution with the maximum consumption of pumping power and minimum thermal resistance. The widening position is closer to the downstream and this configuration reduces the pressure drop while maintaining the fluid disturbance effect of the expansion-contraction sections. Compared to the straight channel, the optimal compromise solution selected by TOPSIS strategy reduces pumping power consumption by 10.0% and the thermal resistance by 47.0% concurrently. Therefore, the optimal MCHS with the TOPSIS solution and the optimization approach are highly applicable in practice.

关键词： Microchannel heat sinks Response surface multi-objective genetic algorithm multi-criteria decision-making The optimal compromise solution Heat transfer enhancement

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Power capacity enhancement of hydropower plant through the penetration of solar and wind energy

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International Journal of Electrical Power & Energy Systems 2025年 169卷

作者： Ahmad Shah Irshad Ahmad Shah Amin Amir Mohammad Ilham M.H. Elkholy Said Elias Tomonobu Senjyu Department of Electrical and Electronics Engineering Faculty of Engineering University of the Ryukyus Okinawa 903-0213 Japan Department of Energy Engineering Engineering Faculty Kandahar University Kandahar 3801 Afghanistan Da Afghanistan Brashna Sharkat Kandahar 3801 Afghanistan Electrical Power and Machines Faculty of Engineering Zagazig University P.O. 44519 Zagazig Egypt Institute for Risk and Reliability Leibniz University Hannover Germany

Integrating renewable energy sources is crucial for enhancing the power capacity and reliability of existing hydropower plants. This study explores the potential of augmenting hydropower capacity by incorporating solar and wind energy. By leveraging the complementary nature of these renewable sources, the study aims to optimize energy production and reduce dependence on hydro resources during low water availability. A comprehensive analysis, including simulation models and field data, demonstrates the effective harnessing and synchronization of solar and wind energy with hydropower generation. A multi-objective genetic algorithm was employed to optimize this integration, addressing objectives such as maximizing power output, improving energy efficiency, and minimizing environmental impact. This algorithm facilitated the identification of effective strategies for enhancing the performance and capacity of the hybrid renewable energy system. Three key objectives were achieved: reducing the Loss of Power Supply Probability (LPSP) to 0.75 %, calculating the net present cost (NPC) at $4.37 million, and determining the cost of energy (COE) at $0.086 per kilowatt-hour (kWh). These results demonstrate the economic viability and reliability of the integrated system. The power generation capacity of the plant was enhanced by 25 % with integration of PV, and wind energy system. The findings reveal significant improvements in power capacity and grid stability, highlighting the feasibility and benefits of a hybrid renewable energy system. This approach enhances energy security, reduces greenhouse gas emissions by 248,922.4 tons per year, and promotes clean energy use. The study offers valuable insights and guidelines for policymakers and energy planners seeking to implement similar strategies.

关键词： Hybrid Renewable Energy System Solar-Wind-Hydro Integration Energy Cost Optimization Net Present Cost Analysis Power Supply Reliability Environmental Sustainability CO 2 Carbon dioxide COE Cost of energy CRF Capital recovery factor DG Diesel generator GA genetic algorithm GHG Greenhouse gas GHI Global horizontal irradiance GOA Grasshopper optimization algorithm HRES Hybrid renewable energy systems IRENA International Renewable Energy Agency LCOE Levelized cost of energy LPSP Loss of power supply probability MILP Mixed-integer linear programming MOGA multi-objective genetic algorithm MPSO Modified particle swarm optimization NASA National Aeronautics and Space Administration NOCT Nominal operating cell temperature NPC Net present cost O&M Operation and maintenance PHS Pumped hydro storage PV Photovoltaic RE Renewable energy RES Renewable energy sources STC Standard test condition UNFCCC United Nations Framework Convention on Climate Change WT Wind turbine

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Seismic control of modularized suspended structures with optimal vertical distributions of the secondary structure parameters

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ENGINEERING STRUCTURES 2019年 183卷 160-179页

作者： Ye, Zhihang Feng, De-Cheng Wu, Gang Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 210096 Jiangsu Peoples R China

Suspended building systems with vibration control features dissipate seismic energy by the interaction between their main parts and the suspended parts;they are also architecturally appealing. A modularized suspended structure has been previously proposed to overcome the fragility of its secondary structure and to enhance overall attenuation. However, the full potential of modularization is yet to be achieved via the previous configuration, especially in terms of multi-mode control. In this study, the protection effect of prefabricated modules is further harnessed in such a way that drastic vertical-irregularities of inter-story stiffness and dampers within the suspended segment are allowed. Vertical distribution vectors of structural parameters were set as the variables in genetic-algorithm optimizations, with the maximum mean square moment of the primary structure being the main objective. The results show considerably improved attenuation of responses in multiple modes instead of only the fundamental mode. In the optimized distributions, peaks of damping coefficient occur at the troughs of inter-story stiffness, but without a highly concentrated pattern. Models with different irregularity levels have well-separated Pareto fronts;this indicates that comprehensive improvement can be obtained at compromised choices. The main mechanism is that, with the well-designed irregularities, the secondary structure provides satisfactory dissipation and tuning to the primary structure in the major modes. The analysis with non-stationary excitations reveals that optimized vertical distributions further quicken the vibration decay. The time-history performance verifications and the structural uncertainty analysis are also carried out.

关键词： Suspended structure Mega-substructure system Modular construction Vertical irregularity Passive vibration control multi-objective genetic algorithm

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An improved NSGA-II based control allocation optimisation for aircraft longitudinal automatic landing system

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INTERNATIONAL JOURNAL OF CONTROL 2019年第4期92卷 705-716页

作者： Bian, Qi Nener, Brett Wang, Xinmin Northwestern Polytech Univ Sch Automat Xian Shaanxi Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Perth WA Australia

In this paper, an Improved Nondominated Sorting genetic algorithm II is proposed for control allocation optimisation in the field of aircraft automatic landing. The initial chromosomes are generated by using a quasi-random sequence to get a better initial searching ability. The adaptive crossover operator and mutation operator are proposed for the dynamic searching area by adaptively relocating the target according to the current searching results. The corresponding online boundary adjustment strategy is created to maintain the robustness of the algorithm during the whole searching process. The control relationships between the elevator and engine channels are presented and a set of feasible solutions are chosen as a reasonable control range for the ALS design. Finally, a 6 Degrees of Freedom (6DoF) rigid model of the F/A-18 with external wind perturbation is used as a test bed to demonstrate the feasibility of the proposed method.

关键词： Aircraft automatic landing control allocation optimisation multi-objective genetic algorithm

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