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A new general method for assigning frequencies with low spillover through structural modifications

JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2023年第4期42卷 1900-1918页

作者： Liu, Zihao Pang, Jichao Ouyang, Huajiang Xie, Guocheng Hainan Univ Haikou Peoples R China Univ Liverpool Liverpool England Univ Liverpool Quadrangle Liverpool L69 3GH England

By modifying the mass and stiffness parameters of a structure, a subset of its natural frequencies can be assigned. However, such an assignment is usually accompanied with changes of the remaining frequencies, known as 'spillover'. Eigenvalue or frequency spillover often needs to be avoided in engineering. In this paper, a new and general method is proposed for assigning a subset of the natural frequencies of a structure with a low level of spillover through structural modifications. The method combines a multi-population genetic algorithm and a radial basis function neural network and is implemented in a MATLAB-PYTHON-ABAQUS data interaction system. It is suitable for theoretical models (for example, finite element models) of general structures. By means of three numerical examples, the ability of the method to assign subsets of frequencies to discrete and discretised continuous structures while maintaining low spillover is demonstrated.

关键词： Partial eigenvalue assignment structural modifications multi-population genetic algorithm radial basis function neural network MATLAB-PYTHON-ABAQUS data interaction system

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An integrated method of automated layout design and optimization for modular construction

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2024年第3期31卷 1016-1036页

作者： Zheng, Zihao Li, Yuanqi Torres, Jaume Tongji Univ Sch Civil Engn Dept Bldg Engn Shanghai Peoples R China

Purpose This paper aims to propose a generative design method combined with meta-heuristic algorithm for automating and optimizing the floor layout of modular buildings using typical standardized module units, which are the room module, the corridor module and the stair module. Design/methodology/approach The integrated framework involves the generative design method and optimization for modular construction. The generative rules are provided by geometric relationships and functionalities of the module units. An evaluation function of the generated floor plans is also presented by the combination of project cost and cost penalties for the geometric features. The multi-population genetic algorithm (MPGA) method is provided for the optimization of the combination of costs. Findings The proposed MPGA method is demonstrated fast and efficient at discovering the globally optimal solution. The results indicate that when the unit price of modules is high, the transportation distance is long, or the land cost is high, the layout cost, which related to the symmetry, the compactness and the energy is tend to be lower, making the optimal layout economical. Originality/value This paper presented an integrated framework of generative floor layout and optimization for modular construction by using typical module units. It fulfills the need for automated layout generation with repetitive units and corresponding assessment during the early design stage.

关键词： Modular construction Floor layout plan Unified matrix method multi-population genetic algorithm

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Integrated production planning and condition-based maintenance considering uncertain demand and random failures

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2020年第1-2期234卷 310-323页

作者： Wang, Lin Lu, Zhiqiang Ren, Yifei Tongji Univ Sch Mech Engn Caoan Rd 4800Mech Bldg A443 Shanghai 201804 Peoples R China

This article proposes a proactive approach for the integrated problem of production planning and condition-based maintenance under uncertain environment, that is, the exogenous uncertainty of demand and the endogenous uncertainty of failure. Considering that the production system has limited capacity and suffers degradation with usage, an integration model of production planning and condition-based maintenance policy is proposed, in which the maintenance threshold and production quantity are proactively decided simultaneously. The robust and stable solution of the complicated non-linear model is obtained by a simulation-based multi-population genetic algorithm. Numerical results not only show the impacts of different factors on the model, but also prove the superiority of the developed model and algorithm in uncertain environment.

关键词： Production planning condition-based maintenance optimization uncertainty multi-population genetic algorithm

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Optimal energy management of a fuel cell-battery-supercapacitor-powered hybrid tramway using a multi-objective approach

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT 2020年第5期234卷 511-523页

作者： Zhang, Han Yang, Jibin Zhang, Jiye Song, Pengyun Li, Ming Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Peoples R China Xihua Univ Sch Automobile & Transportat Chengdu Peoples R China Southwest Minzu Univ Coll Elect & Informat Engn Chengdu Peoples R China CRRC Tangshan Co Ltd Tangshan Peoples R China

Achieving an optimal operating cost is a challenge for the development of hybrid tramways. In the past few years, in addition to fuel costs, the lifespan of the power source is being increasingly considered as an important factor that influences the operating cost of a tramway. In this work, an optimal energy management strategy based on a multi-mode strategy and optimisation algorithm is described for a high-power fuel cell hybrid tramway. The objective of optimisation is to decrease the operating costs under the conditions of guaranteeing tramway performance. Besides the fuel costs, the replacement cost and initial investment of all power units are also considered in the cost model, which is expressed in economic terms. Using two optimisation algorithms, a multi-population genetic algorithm and an artificial fish swarm algorithm, the hybrid system's power targets for the energy management strategy were acquired using the multi-objective optimisation. The selected case study includes a low-floor light rail vehicle, and experimental validations were performed using a hardware-in-the-loop workbench. The results testify that an optimised energy management strategy can fulfil the operational requirements, reduce the daily operation costs and improve the efficiency of the fuel cell system for a hybrid tramway.

关键词： Fuel cell hybrid energy storage system tramway energy management strategy multi-population genetic algorithm artificial fish swarm algorithm

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EFSM-Based Test Data Generation with multipopulation genetic algorithm

EFSM-Based Test Data Generation with MultiPopulation Genetic...

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2014 IEEE 5th International Conference on Software Engineering and Service Science

作者： Xiaofei Zhou Ruilian Zhao Feng You College of Information Science and Technology Department Beijing University of Chemical Technology

ISBN: (纸本)9781479932801

Extended Finite State Machine(EFSM) is a popular formal specification which is widely used to describe states and actions of software *** test generation on EFSM model is difficult due to the existence of the context ***-population genetic algorithm(MPGA) is a novel heuristic search algorithm which is introduced to automatically generate test data for transition paths on EFSM ***,the parameter setting of MPGA is a critical problem for the efficiency of test data generation.A simple ‘rules of thumb' approach is applied to find an optimal parameter setting of MPGA on test data generation for EFSM *** experimental results suggest that MPGA can effectively generate test data for the transition paths of EFSM models and the optimal parameters setting obtained by ‘rules of thumb' can ensure the efficiency of test data automatic generation for EFSM models.

关键词： Extended Finite State Machine multi-population genetic algorithm Transition path Test data generation

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Evolution of recurrent neural controllers using an extended parallel genetic algorithm

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ROBOTICS AND AUTONOMOUS SYSTEMS 2005年第2-3期52卷 148-159页

作者： Capi, G Doya, K Fukuoka Inst Technol Fac Informat Engn Higashi Ku Fukuoka Japan ATR Computat Neurosci Labs Kyoto 6190288 Japan

Autonomous intelligent agents often must complete non-Markovian sequential tasks, which require complex recurrent neural controllers. In order to improve the convergence of evolution and reduce the computation time, this paper proposes application of an extended evolutionary algorithm. We implemented an extended multi-population genetic algorithm (EMPGA), where subpopulations apply different evolutionary strategies. In addition, subpopulations compete and cooperate among each other. Results show that EMPGA outperformed single population genetic algorithm (SPGA) by efficiently distributing the number of individuals among subpopulations as different strategies became successful during the course of evolution. In addition, the comparison with other multi-population GA shows that competition between subpopulations improved the quality of solution. The evolved neural controllers were also tested in the real hardware of Cyber Rodent robot. (C) 2005 Elsevier B.V. All rights reserved.

关键词： multi-population genetic algorithm recurrent neural network sequential task mobile robot

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A novel strategy for the crossarm length optimization of PSSCs based on multi-dimensional global optimization algorithms

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ENGINEERING STRUCTURES 2021年 238卷 112238-112238页

作者： Li, Pengcheng Wang, Hao Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Shandong Jianzhu Univ Sch Civil Engn Jinan 250101 Shandong Peoples R China

Prestressed stayed steel columns (PSSCs) offer a lightweight alternative for the design of slender compression columns. The stability of compression columns can be significantly enhanced by introducing crossarms. Previous research on this subject has primarily focused on load-carrying capacity estimation or analyses of the influencing factors of PSSCs. Thus, investigations of the design optimization of PSSCs remain scant. This work proposes a novel strategy for crossarm length optimization of PSSCs based on multi-dimensional global optimization algorithms. A multi-population genetic algorithm is introduced in the optimization process. The proposed optimization algorithm was demonstrated to be efficient and accurate in terms of determining the optimal crossarm length of PSSCs.

关键词： PSSC Crossarm length Buckling load Optimization algorithm multi-population genetic algorithm

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Application of mathematical models on efficiency evaluation and intervention of medical institutions in China

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BMC HEALTH SERVICES RESEARCH 2024年第1期24卷 1376页

作者： Tai, Qiwen Wang, Qinghua Li, Jiang Dou, Nannan Wu, Huazhang Soochow Univ Affiliated Hosp 1 Jiangsu Inst Hematol 188 Shizi Rd Suzhou Jiangsu Peoples R China China Med Univ Inst Foreign Languages 77 Puhe Rd Shenyang Liaoning Peoples R China China Med Univ Sch Nursing 77 Puhe Rd Shenyang Liaoning Peoples R China China Med Univ Off Gen Management Affiliated Hosp 4 77 Puhe Rd Shenyang Liaoning Peoples R China China Med Univ Sch Publ Hlth 77 Puhe Rd Shenyang Liaoning Peoples R China

Background The efficiency of medical services directly impacts the economic burden of healthcare, making it crucial to analyze the input-output efficiency of various types of medical institutions. However, while hospitals had been extensively analyzed for their efficiency, other types of medical institutions had received limited attention in this *** In this study, we employed data envelopment analysis (DEA) methods based on time series and internal benchmarks to autonomously assess the efficiency of 18 distinct categories of healthcare facilities in China over the past decade. The verification was conducted through the utilization of the critical incident technique (CIT). Additionally, we utilized the Delphi process (AHP) method to evaluate suppliers of medical consumables, implemented a multi-population genetic algorithm for managing these consumethod and analytic hierarchymables efficiently, and applied stakeholder theory to manage medical personnel efficiency. Results Our findings indicated that medical institutions capable of providing clinical services exhibited higher levels of efficiency compared to those unable to do so. multiple indicators suggested redundancy within these institutions. Notably, comprehensive benefit evaluation revealed that clinical laboratory had performed poorly over the past decade. We selected an inefficient medical institution for intervention in reagent management and the work efficiency of medical staff. After implementing the Delphi method and multi-population genetic algorithm for consumable replenishment, the reagent cost was reduced by 40%, 39% and 31% respectively in each of the three experimental groups, compared to the control group. By applying stakeholder theory and process reengineering methods, we were able to shorten quality control management time for medical staff in the experimental group by 41 min per day, reduce clinical service time by 25 min per day, and extend rest time by 70 min per day, while the qu

关键词： DEA CIT Delphi AHP multi-population genetic algorithm Stakeholder Medical institution Medical consumable Medical personnel efficiency

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Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

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Energy Engineering 2023年第12期120卷 2741-2760页

作者： Xiaobao Yu Kang Yang College of Economics and Management Shanghai University of Electric PowerShanghai200090China

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage *** predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is ***,the study classifies users within the industrial park according to their specific power consumption and supply *** user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of *** incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance *** primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the ***,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

关键词： Distributed energy resources improved back-propagation algorithm multi-population genetic algorithm distribution energy carbon neutral

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SAMPGA Task Scheduling algorithm in Cloud Computing 36

SAMPGA Task Scheduling Algorithm in Cloud Computing

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第36届中国控制会议

作者： Xing Jia Wei Wang Bei Li Jun Department of Automation University of Science and Technology of China

ISBN: (纸本)9781538629185

As cloud computing is growing rapidly,efficient task scheduling algorithm plays a vital role to improve the resource utilization and enhance overall performance of the cloud computing ***,task scheduling is the severe challenge needed to solve urgently in cloud ***,the simulated annealing multi-population genetic algorithm(SAMPGA) is proposed for task scheduling in cloud computing,which is the combination of simulated annealing algorithm(SA)and multi-population genetic algorithm(MPGA) in this *** population initialization,SAMPGA adopts max-min algorithm to enhance the search *** incorporated into SAMPGA is employed to avoid local optimum and improve the performance of global optimum,while a family evolution strategy based on adaptive mechanism in MPGA is proposed to find better solution and improve convergence ***,experiments are conducted to evaluate the efficiency of the proposed method in *** with MPGA,SA and simulated annealing genetic algorithm(SAGA),the results of simulation show that the SAMPGA has more excellent performance in terms of the completion time,completion cost,convergence speed and degree of load imbalance.

关键词： Cloud computing task scheduling multi-population genetic algorithm simulated annealing algorithm

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