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Machine learning - enabled inverse design of shell-based lattice metamaterials with optimal sound and energy absorption

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VIRTUAL AND PHYSICAL PROTOTYPING 2024年第1期19卷

作者： Hu, Zongxin Ding, Junhao Ding, Shuwei Ma, Winston Wai Shing Chua, Jun Wei Li, Xinwei Zhai, Wei Song, Xu Chinese Univ Hong Kong Dept Mech & Automat Engn Shatin Hong Kong Peoples R China Natl Univ Singapore NUS Dept Mech Engn Singapore 117575 Singapore Newcastle Univ Fac Sci Agr & Engn Singapore 567739 Singapore

Currently, the development in shell-based lattice, is increasingly focused on multifunctionality, with growing interest in combining sound and energy absorption. However, few studies have explored the multi-objective inverse design process. Herein, we propose a new approach using machine learning (ML) to optimise both the mechanical and acoustic performances of shell-based lattices. Firstly, the K-Nearest Neighbour and Artificial Neural Network are employed to predict the properties of different configurations. Then the non-dominated sorting genetic algorithm is employed to generate the desired structures. Finally, the lightweight metamaterials generated achieve optimal multifunctional performances (an energy absorption capacity of 50% higher than typical Gyroid structure and a sound absorption coefficient near 1 at specific frequency band). Besides, the potential trade-off phenomenon of mechanical and acoustic properties is also presented by our work. Overall, this work presents a new concept to use ML and genetic algorithm for multi-functional inverse design for shell lattice metamaterials.

关键词： Lattice metamaterial machine learning non-dominated sorting genetic algorithm energy absorption sound absorption

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Process scheduling for prefabricated construction based on multi-objective optimization algorithm

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AUTOMATION IN CONSTRUCTION 2024年 168卷

作者： Li, Yan Wu, Jiajun Hao, Yi Gao, Yuchen Chai, Runqi Chai, Senchun Zhang, Baihai Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China China Construct First Grp Corp Ltd Beijing 100161 Peoples R China China Acad Launch Vehicle Technol Beijing 100076 Peoples R China

Prefabricated construction has become an increasingly important focus area in the development of the construction industry. Determining an optimal construction process scheduling program is an urgent challenge during the project execution stage. This paper presents a multi-objective optimization problem with the objective function of minimizing the total construction time and maximizing the coordinated scheduling coefficient, and proposes a non-dominated sorting genetic algorithm based on the subspecies differentiation strategy (SD-NSGA) to solve the problem. The algorithm extends the competition phenomenon at the individual level to the subpopulation level in the traditional genetic algorithm (GA). The results demonstrate that SDNSGA exhibits superior optimization capabilities. Compared with the initial scheme of a real residential construction project, the total working time is shortened by 35.49% and the integrated dispatch factor is increased by 365.79%. Therefore, the proposed algorithm can offer a valuable reference for determining scheduling plans in practical engineering projects.1 1

关键词： Prefabricated construction Process scheduling Multi-objective optimization non-dominated sorting genetic algorithm Subspecies differentiation strategy

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Data driven multi-objective design for low-carbon self-compacting concrete considering durability

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JOURNAL OF CLEANER PRODUCTION 2024年 450卷

作者： Cheng, Boyuan Mei, Liu Long, Wu-Jian Luo, Qiling Zhang, Jinrui Xiong, Chen Shu, Yuqing Li, Zhangjian Gao, Song Shenzhen Univ Coll Civil & Transportat Engn Key Lab Coastal Urban Resilient Infrastruct MOEGuangdong Prov Key Lab Durabil Marine Civil En Shenzhen 518060 Guangdong Peoples R China Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300350 Peoples R China YCIH Green High Performance Concrete Co Ltd Kunming 650217 Yunnan Peoples R China Qingdao Univ Technol Sch Civil Engn Qingdao 266011 Shandong Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Guangdong Peoples R China

Self-Compacting Concrete (SCC) offers remarkable benefits in modern engineering. However, traditional SCC design faces challenges, necessitating a reduction in carbon emissions for enhanced sustainability and a shift towards multi-performance collaborative design. This study proposed an intelligent and interpretable approach for multi-objective low-carbon SCC design. By combining machine learning and optimization algorithm, key factors including sustainability, rheology, workability, strength, durability, and cost were simultaneously addressed. Partial Dependence Plots was employed for model interpretation and feature impacts reveal. The proposed three-objective optimization exhibited superior efficiency, achieving comprehensive optimization efficiencies of 42.3%. In the context of meeting other performance requirements, C40 and C50 SCC optimized using this method exhibited a significant reduction of 18.9% and 10.1% in embodied carbon. This study aims to establish a versatile intelligent framework, rather than a specific model, capable of adapting to future iterations with evolving high-quality data, to achieve multi-objective SCC collaborative design. This advancement contributes to intelligent and low-carbon practices in concrete science and the construction industry.

关键词： Low-carbon self-compacting concrete Sustainability Durability Multi-objective design Machine learning non-dominated sorting genetic algorithm

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Multi-objective spectral range optimization for different heat source temperatures to maximize thermophotovoltaic performance using NSGA-II

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RENEWABLE ENERGY 2024年 237卷

作者： Liu, Xiu-Li Li, Xiao-Lei Xia, Xin-Lin Chen, Xue Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China Minist Ind & Informat Technol Key Lab Aerosp Thermophys Harbin 150001 Peoples R China

The energy conversion efficiency (ECE) and output power density of thermophotovoltaic (TPV) cells are mutually constraining. To better utilize this relationship, a framework was proposed to integrate the internal quantum efficiency (IQE) of TPV cells with a multi-objective genetic algorithm (NSGA-II) to optimize both efficiency and power density across diverse operating conditions. A spectrally selective emitter with a temperature of 1000-3000 K was selected as the radiation source for the studied TPV devices, capable of emitting a spectrum between 0.4 and 2.0 mu m. The gallium antimonide (GaSb) cell was selected as the exploration cell, operating within a temperature range of 0-200 degrees C. Through theoretical calculation, the changes in physical parameters and IQE curves of GaSb cells at various temperatures were determined. The optimal spectral range for varying cell and emitter temperatures was determined using the NSGA-II algorithm. It was found that the IQE curve decreases with increasing temperature. Due to the spectral mismatch, the TPV conversion efficiency is much less than 20 % when the spectral range of the cell is 0.4-2.0 mu m at room temperature. By incorporating IQE into the multiobjective optimization, the efficiency and power density distribution curves can be divided into three regions. In practical applications, the corresponding regions can be selected based on different efficiency and output power requirements.

关键词： Thermophotovoltaic Internal quantum efficiency Optimal spectral range non-dominated sorting genetic algorithm Energy conversion

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Multi-objective robust optimization of staff scheduling for emergency under stochastic demand

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 254卷

作者： Hu, Yucong Liu, Qingyang Li, Sitong Wu, Weitiao South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510641 Peoples R China

Large-scale centralized emergency rescue staff scheduling under emergencies has always been a challenge. With a specific focus on nucleic acid testing amid the COVID-19 pandemic, this study introduces a model that considers dynamic changes in staff supply at rescue points, the varying demand at sampling points over time, and their impact on the spread of the epidemic. We quantified the emergency weight of sampling points by the demand and the regional risk degree and adopted robust optimization with base constraint. With the objectives of minimum comprehensive service distance, maximum weighted value of demand satisfaction rate, and minimum loss caused by unmet demand, a multi-stage, multi-medical rescue point medical staff scheduling model for nucleic acid sampling points with the influence caused by uncertain demand disturbance was built, and an improved NSGA-II-HC algorithm was designed to solve the optimization problem under the influence of demand disturbances. The testing results proved that the NSGA-II-HC algorithm can tackle the issue of insufficient uniformity and diversity in the solution set of NSGA-II while Multi-Objective Particle Swarm Optimization (MOPSO) failed to do so. Taking the epidemic data of Guangzhou city in May 2021 as a case study, our model and algorithm are verified to be feasible. We offer a scheme selection strategy according to the loss degree of two conflicting objectives, the comprehensive service distance and shortage loss. The results suggest that, compared with the demand deterministic model, the decision mechanism under the robust model can reduce the deviation from the optimization objective due to the demand disruption.

关键词： Public health event Sampling crew scheduling Multi-objective robust optimization non-dominated sorting genetic algorithm COVID-19

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A Wide-band Power Amplifier based on genetic algorithm and Direct Layout Optimization

A Wide-band Power Amplifier based on Genetic Algorithm and D...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Sun, Bingjie Chen, Shichang Cai, Jialin Zhao, Peng Cheng, Yifeng Liu, Jun Wang, Gaofeng Hangzhou Dianzi Univ Sch Elect & Informat Hangzhou Peoples R China

ISBN: (纸本)9781665478342

In this paper, a non-dominated sorting genetic algorithm assisted by Gaussian Process (GP-NSGA-II) is proposed. The Gaussian regression prediction is introduced into the conventional NSGA-II to improve optimization speed by reducing the number of *** new method is further integrated with a direct layout optimization technique by combining several commercial electronic design automation (EDA) tools. In virtue of these strategies, layout can be designed automatically. To validate the effectiveness, a wideband GaN power amplifier covering 2-3 GHz is implemented based on a 10W HEMT device. Under input power of 29 dBm, the obtained power-added efficiency (PAE) of the designed circuit is higher than 60% throughout the operational frequency band, and the output power is larger than 41.6 dBm.

关键词： Electronic design automation non-dominated sorting genetic algorithm layout automation optimization power amplifier

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Multi-Objective Factored Evolutionary Optimization and the Multi-Objective Knapsack Problem

Multi-Objective Factored Evolutionary Optimization and the M...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Peerlinck, Amy Sheppard, John Montana State Univ Gianforte Sch Comp Bozeman MT 59717 USA

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

We propose a factored evolutionary framework for multi-objective optimization that can incorporate any multi-objective population based algorithm. Our framework, which is based on Factored Evolutionary algorithms, uses overlapping subpopulations to increase exploration of the objective space;however, it also allows for the creation of distinct subpopulations as in co-operative co-evolutionary algorithms (CCEA). We apply the framework with the non-dominated sorting genetic algorithm-II (NSGA-II), resulting in Factored NSGA-II. We compare NSGA-II, CC-NSGA-II, and F-NSGA-II on two different versions of the multi-objective knapsack problem. The first is the classic binary multi-knapsack implementation introduced by Zitzler and Thiele, where the number of objectives equals the number of knapsacks. The second uses a single knapsack where, aside from maximizing profit and minimizing weight, an additional objective tries to minimize the difference in weight of the items in the knapsack, creating a balanced knapsack. We further extend this version to minimize volume and balance the volume. The proposed 3-to-5 objective balanced single knapsack problem poses a difficult problem for multi-objective algorithms. Our results indicate that the non-dominated solutions found by F-NSGA-II tend to cover more of the Pareto front and have a larger hypervolume.

关键词： multi-objective combinatorial optimization co-operative coevolution non-dominated sorting genetic algorithm multi-objective knapsack

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A dynamic dispatching problem to allocate relief vehicles after a disaster

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ENGINEERING OPTIMIZATION 2021年第11期53卷 1999-2016页

作者： Gharib, Mehrdad Fatemi Ghomi, Seyyed Mohammad Taghi Jolai, Fariborz Amirkabir Univ Technol Dept Ind Engn Tehran Iran Univ Tehran Coll Engn Dept Ind Engn Tehran Iran

This article proposes a deterministic model for a post-disaster scenario in an urban emergency medical services system to allocate the emergency vehicles to the patients and transfer them to the hospital. To solve the model, an exact approach called the -constraint method is applied to the problem. Since this problem belongs to the class of NP-hard problems, two metaheuristic algorithms, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective imperialist competitive algorithm (MOICA), are applied to large-scale problems. The performance of the algorithms is evaluated using computational experiments. Finally, the model is applied in a real-life case study for an expected earthquake in Iran and several managerial insights are extracted.

关键词： Disaster emergency medical services epsilon-constraint method non-dominated sorting genetic algorithm multi-objective imperialist competitive algorithm

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Research on Balanced Allocation of English Educational Resources Based on Multi-objective Optimisation and NSGA-II

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2025年第2期24卷

作者： Liu, Dongyang Henan Univ Anim Husb & Econ Sch Foreign Languages Zhengzhou 450046 Peoples R China

With the in-depth development of globalisation, English, as an international common language, is of great importance to the reasonable allocation of its educational resources for the improvement of national educational equity and overall educational quality. However, there has long been a serious imbalance in the allocation of English education resources in China. In terms of resource allocation between urban and rural areas and different schools, differences in the number of teachers, teaching facilities, teaching materials and other aspects lead to gaps in the opportunities and quality of English education for students. This imbalance in resource allocation not only affects educational equity, but also limits students' English learning outcomes. Therefore, this paper proposes an optimal allocation method of English educational resources based on multi-objective optimisation (MOO) and non-dominated sorting genetic algorithm (NSGA-II). This method aims to maximise the utilisation rate of educational resources, narrow the gap between urban and rural areas and schools, and promote educational equity and efficient use of resources by optimising multiple objectives at the same time, such as teacher allocation, balanced use of teaching facilities and fair distribution of teaching materials resources. The experimental results show that the proposed method has a high degree of fit on two different datasets, reaching 94.58% and 96.87%, respectively, and the resource balance is greatly improved in the process of resource allocation. In addition, the algorithm has high operating efficiency under large-scale data, and the training time can be stabilised at 57.84 s when the sample size reaches 24,200. The experiment also shows that the application of this method in districts and counties significantly reduces the fluctuation of educational resources and optimises the allocation level of educational resources in each district and county. The research provides a new way to solve

关键词： Balanced distribution education resources English multi-objective optimisation non-dominated sorting genetic algorithm

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Optimization of Pre-Treatment Parameters before Diamond Coating using non-dominated sorting genetic algorithm (NSGA-II)

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2nd International Conference on Advanced Material Research (ICAMR 2012)

作者： Golshan, Abolfazl Shirdar, Mostafa Rezazadeh Gohery, Scott Alvansazyazdi, Mohammadfarid Isfahan Univ Technol Fac Mech Engn Esfahan Iran Univ Teknol Malaysia Fac Mech Engn Skudai 81310 Malaysia Univ Teknol Malaysia Fac Civil Engn Skudai 81310 Malaysia

ISBN: (纸本)9783037853634

In this study a single step of the chemical pre-treatment is implemented to tungsten carbide (WC 6 [%]) at the surface of the substrate in order to solve poor adhesion problem. During the pre-treatment process, numerous parameters such as etching time, acid temperature and concentration affect on the surface roughness and Cobalt content of WC-Co substrate are investigated. Optimal selection of these parameters is one of the significant issues to achieve high-quality work-piece in etching process. Thus, the statistical model based on nonlinear polynomial equations is developed for the different responses. non-dominated sorting genetic algorithm (NSGA-II) with the use of MATLAB Software codes is used to solve multi-objective optimization problem in order to provide a preferred solution for a process engineer in a short period of time.

关键词： pre-treatment process surface roughness cobalt content statistical model multi-objective optimization non-dominated sorting genetic algorithm

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