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Developing the hybrid BIM-BEM and jellyfish search optimization system for optimizing energy consumption and building installation costs

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Luong, Duc Long Truong, Ngoc-Son Ngo, Ngoc-Tri Nguyen, Ngoc-Quang Ho Chi Minh City Univ Technol HCMUT Fac Civil Engn 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Linh Trung Ward Ho Chi Minh City Vietnam Univ Danang Univ Sci & Technol Fac Project Management 54 Nguyen Luong Bang St Da Nang Vietnam

In recent years, the use of Building Information Modeling (BIM) with Building Energy Modeling (BEM) has become the primary research focus for reducing the energy consumption of buildings in the planning and operational phases. The combination of BIM and BEM offers advantages for the various phases of a construction project. However, there are currently very few studies that can integrate multi-objective optimization algorithms into the BIM-BEM process to achieve automatic optimization and effectively manage many aspects of building development. In this study, an EnergyPlus integrated multi-objective jellyfish search (EP-MOJSO) system was developed, utilizing an optimization algorithm to find the best thermal insulation layers for an Aluminum composite material (ACM) wall. The goal is to reduce the energy consumption and total cost in a BIM-BEM environment. In the case study, the authors successfully applied the system to a real building, resulting in a 10.7% reduction in total cost and a 65 kWh/m2/year reduction in EUI. It is expected that the results of the study will open up new ways of using algorithms for multi-criteria optimization in BIM models to optimize various project factors such as energy and total cost and thus make an important contribution to sustainable building design.

关键词： Building energy modeling Building Information Modeling multi-objective optimization algorithm Aluminum composite material wall Energy consumption Total cost

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A combined prediction system for PM2.5 concentration integrating spatio-temporal correlation extracting, multi-objective optimization weighting and non-parametric estimation

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ATMOSPHERIC POLLUTION RESEARCH 2023年第10期14卷

作者： Wang, Jianzhou Qian, Yuansheng Gao, Yuyang Lv, Mengzheng Zhou, Yilin Dongbei Univ Finance & Econ Sch Stat 217 Jianshan St Dalian 116025 Liaoning Peoples R China Nanjing Univ Finance & Econ Sch Econ 3 Wenyuan Rd Nanjing 210023 Jiangsu Peoples R China

Air pollution nowadays has seriously hindered the sustainable development. PM2.5 greatly affects air quality and human health, even facilitates virus transmission, making its concentration prediction is crucial. However, previous studies are limited to single PM2.5 concentration series, neglecting optimization of prediction stability, and lacking uncertainty analysis. To address these issues, this research proposed a combined PM2.5 prediction system (CPPs) based on modular concept. Firstly, the temporal and spatial correlations of PM2.5 were fully extracted by data pretreatment and feature selection modules. Subsequently, the results of single submodel prediction module were integrated by multi-objective slime mould algorithm in combination weighted module, achieving Pareto optimality theoretically. Eventually, interval forecasting module analyzed the predictive uncertainty. Notably, a truly accurate metric for predictive directional accuracy was proposed for the first time. Validating CPPs using PM2.5 data from Shanghai during COVID-19 epidemic showed superior performance in point and interval forecasts. This research achieves optimization of prediction accuracy and stability as well as uncertainty analysis based on multiple data sources, contributes to improved air quality and public health protection.

关键词： PM2.5 prediction COVID-19 Fuzzy information granulation Feature selection multi-objective optimization algorithm Prediction direction accuracy

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Optimizing Electric Vehicle Charging Infrastructures in the Smart Grid with a multi-objective Approach 12

Optimizing Electric Vehicle Charging Infrastructures in the ...

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12th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Belaid, Meriem El Beid, Said Hatim, Anas Cadi Ayyad Univ CISIEV Team Marrakech 40160 Morocco Cadi Ayyad Univ TIM Team Marrakech 40160 Morocco

This paper introduces a tool designed to optimize electric vehicle (EV) charging infrastructures within the smart grid framework. The tool utilizes a multi -objective approach and is programmed in Python. It enables dynamic management of energy distribution among different EV charging infrastructures, addressing scenarios where surplus photovoltaic (PV) power generation exceeds charging demands but faces challenges due to storage costs and electric energy transmission rates to alternative infrastructures. In instances of low PV production relative to charging demand, thc algorithm strategically selects thc optimal procurement strategy. either purchasing electric energy from neighboring infrastructures or utilizing surplus PV energy for direct charging. The tool empowers stakeholders to make informed decisions by facilitating comparisons between the cost of storing electric energy locally and the expense of procuring it from external sources, thereby enhancing the efficiency and cost-effectiveness of EV charging infrastructures in the smart grid ecosystem. Extensive simulations and case studies demonstrate the efficacy of the proposed approach, showcasing its potential to optimize energy distribution and promote sustainable practices within the EV charging domain.

关键词： Energy management multi-objective optimization algorithm Smart grid Charging infrastructure Electric vehicle Python PYPSA PV power

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Experimental Comparison of Different Differential Evolution Strategies in MOEA/D 13

Experimental Comparison of Different Differential Evolution ...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Xu, Meng Zhang, Maoqing Cui, Zhihua Zhang, Guoyou Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan Shanxi Peoples R China

ISBN: (纸本)9781538621653

MOEA/D is a well-known optimization algorithm in dealing with complex multi-objective problems. It employs a simple differential evolution strategy to generate offspring individuals. However, duo to the sensibility to the parameter setting in differential evolution strategy, MOEA/D performs poor in certain problems. To understand the influences of different DE strategies, this paper tries to investigate the overall performance of MOEA/D with different DE strategies. The experiment results demonstrate that DE/current-to-rand/1 strategy performs the best in all test problems.

关键词： MOEA/D Differential evolution multi-objective optimization algorithm

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Optimal Design of Doherty Power Amplifier Based on Dual-mode Impedance Error Strategy

Optimal Design of Doherty Power Amplifier Based on Dual-mode...

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2022 Conference on Antenna Measurements and Applications

作者： Fu, Hongyan Kong, Wa Xia, Jing Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781665490375

For better performance, this paper proposes an optimization objective function strategy using the dual-mode matching theory of the Doherty power amplifiers (DPAs). The proposed dual-mode impedance error strategy can be used in the multi-objective optimization algorithm for the carrier and peaking output matching network designs in the DPA. For verification, a 1.5-2.6 GHz wideband DPA was designed to meet the impedance optimization requirements in the back-off power and saturation modes. The 6 dB back-off efficiency achieves 44.3%-56.4% over the whole frequency band, the saturation power of this DPA is 42.3-44.2 dBm with a gain of 8.4-10.2 dB in the interested band.

关键词： Doherty Power Amplifier Dual Mode multi-objective optimization algorithm Matching Network

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Goal-pareto based NSGA for optimal reconfiguration of cognitive radio systems

Goal-pareto based NSGA for optimal reconfiguration of cognit...

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2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications

作者： Park, Soon Kyu Shin, Yoan Lee, Won Cheol Soongsil Univ Sch Elect Engn Seoul 156743 South Korea

ISBN: (纸本)9781424408146

This paper proposes a novel optimization algorithm named goal-pareto based non-dominated sorting genetic algorithm (GBNSGA). The proposed technique has a role of playing an optimal reconfiguration of agile cognitive radio (CR) system by altering set of modem parameters designated as knobs working in dynamic wireless environment. Recently, various kinds of bio-inspired algorithms either timely or costly efficien have been investigated so as to accomplish a smart way of switching the radio configuration. Towards this, the proposed GBNSGA in this paper performs a hybrid type of optimization process from a functional point of view, namely well-known pareto-based non-dominated sorting genetic algorithm (NSCA) performs with goal programming (GP) cooperatively. To confirm the validity, a realistic scenario covering cdma2000 forward link for the class of conversational audio service delivered in Rician channel is set to be considered. By conducting computer simulations, the figure of merits of the proposed algorithm will be exploited regarding how many sets of not over-maximizing systems are achieved residing in range of prescribed performance goals after an optimization procedure. Among those, the optimal configuration can be achieved in the sense of consuming low transmit power, while obeying required quality of service.

关键词： cognitive radio cognitive engine multi-objective optimization algorithm genetic algorithm NSGA

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A Novel Image Recognition Method for Steel Material 41

A Novel Image Recognition Method for Steel Material

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第41届中国控制会议

作者： Yujia Wang College of Information Science and Engineering Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper proposes a novel image recognition method based on multi-objective evolutionary learning.A DenseNet with multi-objective evolutionary algorithm is applied to steel *** the densely connected operation,DenseNet constructs a deep network learning framework,the data features can be extracted via the propagation with a smaller number of parameters,so as to acquire better classification *** to the difficulties in manually tuning the network structure and selecting hyperparameters,a multi-objective differential evolutionary algorithm with a knee solution is used to optimize the DenseNet *** order to balance the model's performance and complexity,lower loss and less computation are considered as optimized multi-objective functions of the network *** are conducted on the steel strip surface defect *** results illustrate that the proposed method is effective and can achieve various kinds of material defects classification *** the future work,we will try to develop an optimization model to recognize and segment steel strip surface defects effectively and then can be applie in smart industry.

关键词： DenseNet multi-objective optimization algorithm Image recognition Steel material

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An Improved multi-objective Evolutionary algorithm for A Robot Detect Path Planning Problem 41

An Improved Multi-objective Evolutionary Algorithm for A Rob...

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第41届中国控制会议

作者： Jinshuai Dong Hongguo Wang Quan-ke Pan School of Mechanical and Electrical Engineering and Automation Shanghai University Liaocheng Vocational & Technical College

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we study the path planning problem of the detection robot after the coal mine disaster,and arrange a robot to detect multiple target *** objective is to minimize the path length and the degree of danger ***,the A-Star algorithm is used to obtain the path matrix of any two target *** an improved multi-objective optimization algorithm is proposed to find paths with Pareto optimal or near-optimal ***,the multi-objective optimization algorithm is compared with other multi-objective optimization *** experimental results demonstrate that the algorithm can effectively obtain the detection path and minimize the length of the path and the degree of danger.

关键词： Detection robot Path planning A-Star algorithm multi-objective optimization algorithm

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Optimizing Infrastructure Placement in Wireless Mesh Networks using NSGA-II 20

Optimizing Infrastructure Placement in Wireless Mesh Network...

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20th IEEE International Conference on High Performance Computing and Communications (HPCC) / 16th IEEE International Conference on Smart City (SmartCity) / 4th IEEE International Conference on Data Science and Systems (DSS)

作者： Nawaf, Liqaa F. Allen, Stuart M. Rana, Omer Cardiff Metropolitan Univ Sch Comp Sci & Informat Cardiff S Glam Wales Cardiff Univ Cardiff S Glam Wales

ISBN: (纸本)9781538666142

Wireless Mesh Networks (WMNs) provide a flexible and low-cost technology to efficiently deliver broadband services to communities. In a WMN, a mesh router is deployed at each house, which acts both as a local access point and a relay to other nearby houses. Since mesh routers typically consist of off-the-shelf equipment, the major cost of the network is in the placement and management of Internet Transit Access Points (ITAP) which act as the connection to the internet. In designing a WMN, the aim is to minimize the number of ITAPs required whilst maximizing the traffic that could be served to each house. A multi-objective optimization algorithm is investigated to solve the WMN infrastructure placement problem, using crossover and mutation operators. A simulation based analysis is used to demonstrate the benefit of the proposed approach.

关键词： Wireless mesh network multi-objective optimization algorithm optimization neighbourhood move NSGA-II

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Higher Order Mutation Testing to Drive Development of New Test Cases: An Empirical Comparison of Three Strategies 8th

Higher Order Mutation Testing to Drive Development of New Te...

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8th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Quang Vu Nguyen Madeyski, Lech Wroclaw Univ Technol Fac Comp Sci & Management Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783662493816;9783662493809

Mutation testing, which includes first order mutation (FOM) testing and higher order mutation (HOM) testing, appeared as a powerful and effective technique to evaluate the quality of test suites. The live mutants, which cannot be killed by the given test suite, make up a significant part of generated mutants and may drive the development of new test cases. Generating live higher order mutants (HOMs) able to drive development of new test cases is considered in this paper. We apply multi-objective optimization algorithms based on our proposed objectives and fitness functions to generate higher order mutants using three strategies: HOMT1 (HOMs generated from all first order mutants), HOMT2 (HOMs generated from killed first order mutants) and HOMT3 (HOMs generated from not-easy-to-kill first order mutants). We then use mutation score indicator to evaluate, which of the three approaches is better suited to drive development of new test cases and, as a result, to improve the software quality.

关键词： Mutation testing Higher order mutation testing Live mutants Equivalent mutants multi-objective optimization algorithm

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