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Global Paradigm Shifts in Urban Stormwater Management optimization: A Bibliometric Analysis

WATER 2023年第23期15卷 4122-4122页

作者： Wang, Mo Jiang, Zhiyu Ikram, Rana Muhammad Adnan Sun, Chuanhao Zhang, Menghan Li, Jianjun Guangzhou Univ Coll Architecture & Urban Planning Guangzhou 510006 Peoples R China Guangzhou Univ Architectural Design & Res Inst Guangzhou 510091 Peoples R China Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Beijing Forestry Univ Sch Landscape Architecture Beijing 100083 Peoples R China

Amidst the growing urgency to mitigate the impacts of anthropogenic climate change, urban flooding stands out as a critical concern, necessitating effective stormwater management strategies. This research presents a bibliometric analysis of the literature on urban stormwater management optimization from 2004 to 2023, with the aim of understanding how the field has responded to these escalating challenges. Aiming to map the evolution and current state of the field, this study employed a methodical approach, using CiteSpace to analyze publication trends, authorship patterns, and geographical distributions, as well as keyword and citation dynamics. The findings reveal a marked increase in research activity after 2014, with significant contributions observed between 2019 and 2022. Key research themes identified include low-impact development, green infrastructure, and stormwater management, with a notable shift towards hybrid grey-green infrastructure solutions that combine traditional and ecological elements. The prevalence of terms such as 'best management practices' and 'Green Roofs' in recent publications indicates a growing emphasis on practical, case-study-based research, particularly in green infrastructure technologies like bioretention cells. These insights underscore the field's movement towards pragmatic, multi-objective optimization frameworks with tangible applications, guiding future research directions in this increasingly complex domain.

关键词： urban stormwater management bibliometric analysis hybrid grey-green infrastructure low-impact development multi-objective optimization green infrastructure

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multi-functional Inconel 625 micro-machining and process optimization using ANFIS

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2024年

作者： Thakur, Surendra Singh Pradhan, Sharad K. Sharma, Aditi Sonia, Pankaj Sehgal, Shankar Ramu, Gandikota 3D Engn Automat LLP Pune MH India THDC Inst Hydropower Engn & Technol Tehri Uttrakhand India Symbiosis Int Deemed Univ Symbiosis Inst Technol Pune India GLA Univ Dept Mech Engn Mathura UP India Panjab Univ UIET Mech Engn Chandigarh 160014 India Inst Aeronaut Engn Dept Comp Sci & Engn Hyderabad Telangana India

The primary objective of this study is to develop an adaptive neuro-fuzzy inference-based system (ANFIS) that assesses the ideal parameters and impact of the micro-electro-discharge machining (Micro-EDM) technique on surface roughness (SR). In this work multi-functional Inconel alloy specimens were machined using micro-elector-discharge machining (Micro-EDM) with zinc-coated copper electrodes in a graphite nano abrasive submersion medium. An ANFIS and Taguchi design of experiments (DOE) was employed to evaluate the Micro-EDM method. The study utilized Taguchi's L9 mixed orthogonal arrays (OA) to consider three process variables, namely open voltage (V), capacitance (C), and powder concentration (CP). ANOVA analysis was conducted to identify important process variables and evaluate their influence on surface properties. The experimental results showed that all approaches were effective in representing the process. Based on the results of the response surface methodology (RSM) and ANFIS predictions, the correlation coefficients (R2) of the experimental roughness were 0.995649 and the root mean square error (RMSE) value was 1.01226e-06, respectively. The results obtained indicate a strong alignment between the predicted and experimental values, effectively fulfilling the proposed objective. These findings hold significant potential for practical applications in the Micro-EDM industry, particularly in the machining of Inconel alloy.

关键词： multi-functional Inconel 625 alloy micro-EDM surface roughness ANFIS

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A multi-constraint and multi-objective optimization layout method for a mine water inrush monitoring network

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SCIENTIFIC REPORTS 2023年第1期13卷 1-14页

作者： Du, Zhili Wu, Qiang Zhao, Yingwang Zhang, Xiaoyan Yao, Yi China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing Peoples R China Natl Engn Res Ctr Coal Mine Water Hazard Controll Beijing Peoples R China

Mine water inrush can cause property losses and casualties, but current theoretical and technological approaches cannot accurately predict such events. Through the networked deployment of water level sensors along a mine roadway, a mine water inrush monitoring network was developed, and a multi-constraint and multi-objective optimal deployment method was established. By setting practical constraints of the mining area, water inrush risk level, and installation at specified locations, and considering two objective functions of minimum total cost and minimum average monitoring time, a mathematical model was established. The non-dominated sorting genetic algorithm II (NSGA-II) was designed to solve the model. The method temporally and spatially optimized the network, which was then verified in the Beiyangzhuang coal mine in north China. The average response time of the monitoring network was 916 s using only 28 water level sensors. The higher the water inrush risk level, the shorter the monitoring network response time. Under the 2, 3, and 4 risk levels, the network's response time to simulated water inrush accidents was less than 3000, 2100, and 900 s, respectively. The multi-constraint and multi-objective optimization layout method further enhanced the effectiveness of the network, providing a novel system for the early warning of mine water inrush.

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Investor Preference-Aware Portfolio optimization with Deep Reinforcement Learning

Investor Preference-Aware Portfolio Optimization with Deep R...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Zhenglong Li Vincent Tam Kwan L. Yeung Department of Electrical and Electronic Engineering The University of Hong Kong

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Recently, deep reinforcement learning (RL) approaches have been adopted to optimize financial portfolios with the objective of maximizing total profits while reducing potential risks by spreading investment capital across a variety of assets. Despite achieving great advances in the trade-off between profits and risks, the existing deep RL-based frameworks rarely consider practical trading constraints when making decisions in real-world financial markets, which cannot fulfill the customized re-quirements of specific users and may violate market regulations. Accordingly, a multi-Agent and Self-Adaptive trading framework for Constrained portfolio optimization, namely the MASAC, is proposed in which the deep RL-based agent dynamically explores profit-maximization policies while the heuristic-based agent conducts min-conflict search to ensure the generated trading strategies satisfying all concerned constraints. Through sharing knowledge within the trading system, the agents of the proposed framework cooperatively produce new portfolios that can maximize overall profits while satisfying all investor requirements throughout the trading period. The experimental results reveal the advantages of the MASAC framework against many state-of-the-art approaches in investment performance and trading constraint satisfaction on the challenging data sets of real-world markets.

关键词： Deep reinforcement learning Regulation Risk management Portfolios optimization Consumer electronics Investment

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LARGE LANGUAGE MODEL-BASED EVOLUTIONARY OPTIMIZER: REASONING WITH ELITISM

arXiv

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arXiv 2024年

作者： Brahmachary, Shuvayan Joshi, Subodh M. Panda, Aniruddha Koneripalli, Kaushik Sagotra, Arun Kumar Patel, Harshil Sharma, Ankush Jagtap, Ameya D. Kalyanaraman, Kaushic Computational Science Group Shell India Markets Pvt. Ltd. India Division of Applied Mathematics Brown University RI02912 United States

Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios, including multi-objective and high-dimensional problems. We introduce a novel population-based method for numerical optimization using LLMs called Language-Model-Based Evolutionary Optimizer (LEO). Our hypothesis is supported through numerical examples, spanning benchmark and industrial engineering problems such as supersonic nozzle shape optimization, heat transfer, and windfarm layout optimization. We compare our method to several gradient-based and gradient-free optimization approaches. While LLMs yield comparable results to state-of-the-art methods, their imaginative nature and propensity to hallucinate demand careful handling. We provide practical guidelines for obtaining reliable answers from LLMs and discuss method limitations and potential research directions. © 2024, CC BY-NC-ND.

关键词： Shape optimization

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Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian optimization: Current challenges and future opportunities

arXiv

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arXiv 2024年

作者： Biswas, Arpan Valleti, Sai Mani Prudhvi Vasudevan, Rama Ziatdinov, Maxim Kalinin, Sergei V. University of Tennessee-Oak Ridge Innovation Institute Knoxville United States Bredesen Center for Interdisciplinary Research University of Tennessee Knoxville United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory United States Physical Sciences Division Pacific Northwest National Laboratory RichlandWA99352 United States Department of Materials Science and Engineering University of Tennessee Knoxville United States

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time-consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest towards active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge on the system in form of partially known physics laws and often varies exploration policies during the experiment. Here, we explore interactive workflows building on multi-fidelity BO (MFBO), starting with classical (data-driven) MFBO, then structured (physics-driven) sMFBO, and extending it to allow human in the loop interactive iMFBO workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly non-smooth multi-fidelity simulation data generated from an Ising model, considering spin-spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and on-the-fly human decisions for improved exploration, current challenges, and potential opportunities for algorithm development with combining data, physics and real time human decisions. The associated notebooks allow to reproduce the reported analyses and apply them to other systems (https://***/arpanbiswas52/iMFBO_Ising). © 2024, CC BY.

关键词： Ising model

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multi-maneuver algorithms for multi-risk collision avoidance via nonconvex quadratic optimization

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IFAC-PapersOnLine 2023年第2期56卷 1995-2000页

作者： Matthieu Masson Denis Arzelier Mioara Joldes Bruno Revelin Jérôme Thomassin LAAS-CNRS Université de Toulouse 31400 Toulouse France Thales Services Numeriques 290 All. du Lac 31670 Labège France CNES 18 Av. Edouard Belin 31400 Toulouse France

In this article, the problem of modeling and computing the optimal collision avoidance maneuvers for multiple short-term encounters is presented. Several metrics defining the overall collision risk are analyzed. These include imposing lower bounds on the miss-distance or the Mahalanobis distance at each corresponding time of closest approach (TCA) or upper-bounds on the collision probability. The avoidance maneuvers are modeled as impulsive ones in a single direction of the local frame and for a priori fixed dates for operational motivations. Station-keeping constraints are also imposed via linear inequalities on the relative states at each TCA. The specific nature of the imposed constraints is dictated by a practical framework provided by the French Space Agency (CNES). This results in formulating the maneuver design problem as an optimization problem with linear objective and non-convex quadratic constraints. Different algorithms are presented to solve this problem. Finally, the relative efficiency of the proposed approaches is evaluated and analyzed on a realistic conjunction built from data extracted from the CNES database and on academic random examples of higher complexity.

关键词： Collision Avoidance Maneuver multiple Short-term Encounters Impulsive Maneuvers Quadratically Constrained Nonconvex optimization

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Solving bi-objective economic-emission load dispatch of diesel-wind-solar microgrid using African vulture optimization algorithm

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HELIYON 2024年第3期10卷 e24993页

作者： Mishra, Shilpa Shaik, Abdul Gafoor Indian Inst Technol Dept Elect Engn Jodhpur 342030 India Indian Inst Technol Ctr Emerging Technol Sustainable Dev Ctr IoT & Applicat Dept Elect Engn Jodhpur 342030 India

Microgrid is a localised power generation infrastructure designed to provide continuous and reliable power supply to a small, specific region. The increasing concern towards environmental sustainability has resulted in the prioritisation of non-emitting Renewable Energy Sources (RESs) while optimal sizing of microgrid. Optimal sizing of generation units at minimum cost with minimum emission satisfying various practical constraints is a challenging bi-objective optimization problem of power system known as Economic-Emission Load Dispatch (EELD). Metaheuristic approaches are predominantly used to solve the EELD problem. This article explores the advanced metaheuristic methods to solve EELD problem and proposes application of African Vulture optimization Algorithm (AVOA) to subsequently address the EELD problem of a microgrid combining diesel, wind, and solar energy sources based on field data of a specific location in Jaisalmer, India. AVOA emulates the foraging and navigation patterns of vultures, incorporating effective exploration and exploitation characteristics. The effectiveness of AVOA is first validated using three standard test systems of 10, 6 (IEEE30-bus), and 40 units with/without transmission losses, prior applying it for microgrid. The obtained results are compared with several other popular optimization techniques to establish the efficacy of proposed method. Further, AVOA is employed to analyse the impact of individual RESs on microgrid's cost and emissions across three distinct generation scenarios. The viability score is employed to evaluate the efficacy of all techniques along with other significant performance indices. Statistical data tests such as ANOVA, Wilcoxon, and robustness are employed to assess the statistical confidence of the AVOA. Additionally, a multi-comparison post-hoc TukeyHSD test is introduced which proves the superiority of AVOA. Results establish AVOA as the most effective solution for addressing the EELD problem in microgrid (a

关键词： African vulture optimization algorithm Economic-emission load dispatch Microgrid Solar-PV energy Statistical data tests Wind energy

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multi-objective Reinforcement Learning-based Approach for Pressurized Water Reactor optimization

arXiv

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arXiv 2023年

作者： Seurin, Paul Shirvan, Koroush Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States

A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in the field of engineering where the evaluation of candidate solutions can be time-consuming. PEARL distinguishes itself from traditional policy-based multi-objective Reinforcement Learning methods by learning a single policy, eliminating the need for multiple neural networks to independently solve simpler sub-problems. Several versions inspired from deep learning and evolutionary techniques have been crafted, catering to both unconstrained and constrained problem domains. Curriculum Learning (CL) is harnessed to effectively manage constraints in these versions. PEARL’s performance is first evaluated on classical multi-objective benchmarks. Additionally, it is tested on two practical PWR core Loading Pattern (LP) optimization problems to showcase its real-world applicability. The first problem involves optimizing the Cycle length (LC) and the rod-integrated peaking factor (F∆h) as the primary objectives, while the second problem incorporates the mean average enrichment as an additional objective. Furthermore, PEARL addresses three types of constraints related to boron concentration (Cb), peak pin burnup (Bumax), and peak pin power (Fq). The results are systematically compared against conventional approaches from stochastic optimization. Notably, PEARL, specifically the PEAL-NdS (crowding) variant, efficiently uncovers a Pareto front without necessitating additional efforts from the algorithm designer, as opposed to a single optimization with scaled objectives. It also outperforms the classical approach across multiple performance metrics, including the Hyper-volume. Future works will encompass a sensitivity analysis of hyper-parameters with statistical analysis to optimize the application of PEARL and extend it to more intricate problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Identifiability of tissue material parameters from uniaxial tests using multi-start optimization

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ACTA BIOMATERIALIA 2021年 123卷 197-207页

作者： Safa, Babak N. Santare, Michael H. Ethier, C. Ross Elliott, Dawn M. Emory Univ Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Univ Delaware Dept Biomed Engn Newark DE USA Univ Delaware Dept Mech Engn Newark DE 19716 USA

Determining tissue biomechanical material properties from mechanical test data is frequently required in a variety of applications. However, the validity of the resulting constitutive model parameters is the subject of debate in the field. Parameter optimization in tissue mechanics often comes down to the "identifiability" or "uniqueness" of constitutive model parameters;however, despite advances in formulating complex constitutive relations and many classic and creative curve-fitting approaches, there is currently no accessible framework to study the identifiability of tissue material parameters. Our objective was to assess the identifiability of material parameters for established constitutive models of fiber-reinforced soft tissues, biomaterials, and tissue-engineered constructs and establish a generalizable procedure for other applications. To do so, we generated synthetic experimental data by simulating uniaxial tension and compression tests, commonly used in biomechanics. We then fit this data using a multi-start optimization technique based on the nonlinear least-squares method with multiple initial parameter guesses. We considered tendon and sclera as example tissues, using constitutive models that describe these fiber-reinforced tissues. We demonstrated that not all the model parameters of these constitutive models were identifiable from uniaxial mechanical tests, despite achieving virtually identical fits to the stress-stretch response. We further show that when the lateral strain was considered as an additional fitting criterion, more parameters are identifiable, but some remain unidentified. This work provides a practical approach for addressing parameter identifiability in tissue mechanics. Statement of significance Data fitting is a powerful technique commonly used to extract tissue material parameters from experimental data, and which thus has applications in tissue biomechanics and engineering. However, the problem of "uniqueness" or "identifiability

关键词： Identifiability optimization Tendon Sclera Stress mechanics

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