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A hybrid framework integrating classical computers and quantum annealers for optimisation of truss structures 认领 引用
1
作者 Van-Dung Nguyen Erin Kuci +1 位作者 Michel Rasquin Ludovic Noels 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期58-75,共18页
This work proposes a hybrid framework combining classical computers with quantum annealers for structural optimisation.At each optimisation iteration of an iterative process,two minimisation problems are formulated,on... This work proposes a hybrid framework combining classical computers with quantum annealers for structural optimisation.At each optimisation iteration of an iterative process,two minimisation problems are formulated,one for the underlying mechanical boundary value problem through the minimisation of the potential energy principle and one to update the design variables.Our hybrid approach leverages the strength of quantum computing to solve these two minimisation problems at each step,thanks to the developed quantum annealing-assisted sequential programming strategy introduced in our previous research.The applicability of the proposed framework is demonstrated through several case studies of truss optimisation,highlighting its capability to perform optimisation with quantum computers.This framework offers a promising direction for future structural optimisation applications,particularly in scenarios where the quantum computer could resolve the size limitations of classical computers due to problem complexities. 展开更多
关键词 Quantum computing Quantum annealing Optimisation Truss structure
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Bio-Inspired Optimisation Methods Applied to Low Carbon Power and Energy Problems:A Survey 认领 引用
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作者 Tianyu Hu Shihao Zhao +2 位作者 Yuanjun Guo Linxin Zhang Zhile Yang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期297-315,共19页
Bio-inspired optimisation methods have been widely applied to complex real-world problems,particularly in low-carbon power and energy systems,where optimisation tasks often involve high-dimensional,constrained and mix... Bio-inspired optimisation methods have been widely applied to complex real-world problems,particularly in low-carbon power and energy systems,where optimisation tasks often involve high-dimensional,constrained and mixed-integer characteristics.Traditional approaches struggle with these challenges due to nonconvexity,nonlinearity and computational complexity.This paper provides a comprehensive review of bio-inspired optimisation techniques applied to key low-carbon energy problems,including economic load dispatch,unit commitment,optimal power flow,distributed generation planning,heat exchanger design,and parameter estimation for PEM fuel cells and solar cell models.By analysing the strengths and limitations of existing methods,we highlight their effectiveness in addressing computational efficiency,constraint handling and convergence behaviour.The paper also identifies research gaps and discusses future directions,providing a structured reference for algorithm developers and practitioners.This review aims to enhance the adoption and refinement of bio-inspired optimisation techniques for sustainable energy solutions. 展开更多
关键词 benchmarks genetic algorithms optimisation
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Multi-Agent Reinforcement Learning Driven Dynamic Resource Optimisation in Healthcare Transportation Networks 认领 引用
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作者 Jianhui Lv Byung-Gyu Kim +1 位作者 Keqin Li Heng Lu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期316-331,共16页
This paper presents HealthNet,a novel framework for the dynamic optimisation of healthcare transportation networks using multi-agent reinforcement learning.HealthNet leverages a spatiotemporal dependency module to cap... This paper presents HealthNet,a novel framework for the dynamic optimisation of healthcare transportation networks using multi-agent reinforcement learning.HealthNet leverages a spatiotemporal dependency module to capture complex spatiotemporal relationships in healthcare demand and resource allocation patterns,combined with centralised training and a decentralised execution approach.The system is modelled as a Markov game and solved using a deep reinforcement learning algorithm.Extensive simulations demonstrate that HealthNet outperforms eight state-of-the-art baseline methods across multiple network configurations and evaluation metrics.In a 4×4 grid network,HealthNet reduces average waiting times by 47.6%compared to model predictive control and 22.1%compared to the best-performing baseline.Traffic congestion rates are reduced to 16.7%compared to 42.3%for the worst baseline and 23.1%for the best baseline.Under irregular network topologies with stochastic disruptions,including demand surges and vehicle unavailability,HealthNet maintains superior performance with 42.1%lower average waiting time and 51.1%improvement in peak response times compared to competing approaches.These findings indicate that HealthNet can enhance both efficiency and resilience in healthcare transportation systems,potentially improving patient outcomes in complex urban environments. 展开更多
关键词 dynamic resource optimisation healthcare transportation networks reinforcement learning spatiotemporal dependency sustainable cities
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Hyperparameter Optimisation and Comparative Analysis of Machine Learning Models for Travel Mode Choice Prediction 认领 引用
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作者 Mujahid Ali 《Computers, Materials & Continua》 SCIE EI 2026年第9期1946-1966,共21页
Understanding the determinants of travel mode choice(TMC)in urban contexts is essential for effective transport planning and policy development.Past studies predominantly employed traditional discrete choice models be... Understanding the determinants of travel mode choice(TMC)in urban contexts is essential for effective transport planning and policy development.Past studies predominantly employed traditional discrete choice models because of their simplicity,diversity,and high interpretability;however,they rely on restrictive assumptions.Although machine learning(ML)techniques have shown promising predictive capabilities,comparative assessments of traditional and ML approaches,particularly considering hyperparameter optimisation,remain limited.This study addresses this gap by comparing a traditional model with four ML algorithms:decision tree(DT),random forest(RF),support vector machine(SVM),and k-nearest neighbour(KNN).In addition,systematic hyperparameter optimisation is performed to evaluate its impact on predictive performance relative to default model settings.Feature importance analysis is also conducted to identify the key determinants of TMC.The analysis is based on a multi-dimensional,three-week household time-use and activity diary dataset comprising 508 individuals from 191 households in the Bandung Metropolitan Area,Indonesia.The results demonstrate that ML models outperform traditional methods,while hyperparameter optimisation substantially improves model performance across all considered algorithms against default models.Notably,the KNN model exhibits a 16.67%increase in accuracy,followed by the SVM model with an 11.15%improvement.Among the optimised evaluated models,SVM achieves the best overall performance,with a macro-averaged accuracy of 0.588 and a precision of 0.591.Feature importance analysis reveals that total travel time is the most influential determinant of TMC.These findings highlight the importance of model tuning and hyperparameter optimisation in ML-based TMC prediction and provide insights into the factors shaping travel behaviour.The outcomes can support more informed decision-making in urban transport planning and policy formulation. 展开更多
关键词 Travel mode choice hyperparameter optimisation random forest travel behaviour sustainable transportation
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Multi-Objective Optimisation Framework for Heterogeneous Federated Learning 认领 引用
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作者 Jamshid Tursunboev Vikas Palakonda +2 位作者 Il-Min Kim Sunghwan Moon Jae-Mo Kang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期1-14,共14页
Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces s... Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches. 展开更多
关键词 deep learning learning(artificial intelligence) learning models multi-objective optimisation
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Application of Various Optimisation Methods in the Multi-Optimisation for Tribological Properties of Al-B4C Composites 认领 引用
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作者 Sandra Gajevic Slavica Miladinovic +3 位作者 Jelena Jovanovic Onur Güler SerdarÖzkaya Blaža Stojanovic 《Computers, Materials & Continua》 SCIE EI 2025年第9期4341-4361,共21页
This paper presents an investigation of the tribological performance of AA2024–B4C composites,with a specific focus on the influence of reinforcement and processing parameters.In this study three input parameters ... This paper presents an investigation of the tribological performance of AA2024–B4C composites,with a specific focus on the influence of reinforcement and processing parameters.In this study three input parameters were varied:B4C weight percentage,milling time,and normal load,to evaluate their effects on two output parameters:wear loss and the coefficient of friction.AA2024 alloy was used as the matrix alloy,while B4C particles were used as reinforcement.Due to the high hardness and wear resistance of B4C,the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components.The optimisation of tribological behaviour was conducted using a Taguchi-Grey Relational Analysis(Taguchi-GRA)and the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).A total of 27 combinations of input parameters were analysed,varying the B4C content(0,10,and 15 wt.%),milling time(0,15,and 25 h),and normal load(1,5,and 10 N).Wear loss and the coefficient of friction were numerically evaluated and selected as criteria for optimisation.Artificial Neural Networks(ANNs)were also applied for two outputs simultaneously.TOPSIS identified Alternative 1 as the optimal solution,confirming the results obtained using the Taguchi Grey method.The optimal condition obtained(10 wt.%B4C,25 h milling time,10 N load)resulted in a minimum wear loss of 1.7 mg and a coefficient of friction of 0.176,confirming significant enhancement in tribological behaviour.Based on the results,both the B4C content and the applied processing conditions have a significant impact on wear loss and frictional properties.This approach demonstrates high reliability and confidence,enabling the design of future composite materials with optimal properties for specific applications. 展开更多
关键词 Aluminium composites B4C reinforcement taguchi-grey artificial neural networks AHP-TOPSIS optimisation wear loss coefficient of friction
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A Comprehensive Review of Next-Gen UAV Swarm Robotics:Optimisation Techniques and Control Strategies for Dynamic Environments 认领 引用
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作者 Ghulam E Mustafa Abro Ayman M Abdallah +1 位作者 Faizan Zahid Saleem Ahmed 《Intelligent Automation & Soft Computing》 2025年第1期99-123,共25页
This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles(UAVs)and swarm robotics,with a specific emphasis on optimisation strategies,path planning,and formation control.The study i... This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles(UAVs)and swarm robotics,with a specific emphasis on optimisation strategies,path planning,and formation control.The study identifies key methodologies that are driving progress in the field by conducting a comprehensive analysis of seven critical publications.The following are included:sensor-based platforms that facilitate effective obstacle avoidance,cluster-based hierarchical path planning for efficient navigation,and adaptive hybrid controllers for dynamic environments.The review emphasises the substantial contribution of optimisation techniques,including Max-Min Ant Colony Optimisation(MMACO),to the improvement of convergence rates and the enhancement of path efficiency.The effectiveness of various navigation systems in diverse operational contexts is demonstrated through comparative analysis,which provides valuable insights into the system’s adaptability and performance.The primary findings underscore the strengths and limitations of current methodologies,thereby identifying voids in research and practical applications.This review offers actionable insights for academicians and practitioners who are striving to advance UAV and swarm robotics technology by addressing these challenges.The study concludes with a discussion of future directions,which underscores the potential for innovative solutions to enhance UAV systems in complex,dynamic environments. 展开更多
关键词 Formation control optimisation approaches path planning swarm robotics and UAV navigation systems
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Design and Optimisation of a Vibrating Wing Insect-Size Air Vehicle with Lumped Parameter Models and Compliant Links 认领 引用
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作者 Marguerite de La Bigne Mathieu Colin +4 位作者 Éric Cattan Sofiane Ghenna Marie Zwingelstein Sébastien Grondel Olivier Thomas 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第5期2396-2428,共33页
This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle(NAV),driven by an electromagnetic system.Our approach is based on artificial wings composed of rigid bodies conne... This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle(NAV),driven by an electromagnetic system.Our approach is based on artificial wings composed of rigid bodies connected by compliant links,which optimise aerodynamic forces though replicating the complex wing kinematics of insects.The originality of this article lies in a new design methodology based on a triple equivalence between a 3D model,a multibody model,and a mass/spring model(0D)which reduces the number of parameters in the problem.This approach facilitates NAV optimisation by using only the mass/spring model,thereby simplifying the design process while maintaining high accuracy.Two wing geometries are studied and optimised in this article to produce large-amplitude wing motions(approximately 40^\circ),and enabling flapping and twisting motion in quadrature.The results are validated thanks to experimental measurements for the large amplitude and through finite element simulations for the combined motion,confirming the effectiveness of this strategy for a NAV weighing less than 40 mg with a wingspan of under 3 cm. 展开更多
关键词 Resonant motion Microano electromechanical system(M/NEMS) Modal combination Design ofstructural vibration Optimisation Compliant links
A Deep Backtracking Bare-Bones Particle Swarm Optimisation Algorithm for High-Dimensional Nonlinear Functions 认领 引用
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作者 Jia Guo Guoyuan Zhou +4 位作者 Ke Yan Yi Di Yuji Sato Zhou He Binghua Shi 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第5期1501-1520,共20页
The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking ... The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking Bare-Bones Particle Swarm Optimisation(DBPSO)algorithm,an innovative approach built upon the integration of the Deep Memory Storage Mechanism(DMSM)and the Dynamic Memory Activation Strategy(DMAS).The DMSM enhances the memory retention for the globally optimal particle,promoting interaction between standard particles and their historically optimal counterparts.In parallel,DMAS assures the updated position of the globally optimal particle is appropriately aligned with the deep memory repository.The efficacy of DBPSO was rigorously assessed through a series of simulations employing the CEC2017 benchmark suite.A comparative analysis juxtaposed DBPSO's performance against five contemporary evolutionary algorithms across two experimental conditions:Dimension-50 and Dimension-100.In the 50D trials,DBPSO attained an average ranking of 2.03,whereas in the 100D scenarios,it improved to an average ranking of 1.9.Further examination utilising the CEC2019 benchmark functions revealed DBPSO's robustness,securing four first-place finishes,three second-place standings,and three third-place positions,culminating in an unmatched average ranking of 1.9 across all algorithms.These empirical results corroborate DBPSO's proficiency in delivering precise solutions for complex,high-dimensional optimisation challenges. 展开更多
关键词 artificial intelligence computational intelligence optimisation
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Superior decomposition of xenobiotic RB5 dye using three-dimensional electrochemical treatment:Response surface methodology modelling,artificial intelligence,and machine learning-based optimisation approaches 认领 引用
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作者 Voravich Ganthavee Antoine P.Trzcinski 《Water Science and Engineering》 EI CAS CSCD 2025年第1期1-10,共10页
The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment.This study reported a three-dimensional electrochemical treatment ... The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment.This study reported a three-dimensional electrochemical treatment process integrating graphite intercalation compound(GIC)adsorption,direct anodic oxidation,and·OH oxidation for decolourising Reactive Black 5(RB5)from aqueous solutions.The electrochemical process was optimised using the novel progressive central composite design-response surface methodology(CCD-NPRSM),hybrid artificial neural network-extreme gradient boosting(hybrid ANN-XGBoost),and classification and regression trees(CART).CCD-NPRSM and hybrid ANN-XGBoost were employed to minimise errors in evaluating the electrochemical process involving three manipulated operational parameters:current density,electrolysis(treatment)time,and initial dye concentration.The optimised decolourisation efficiencies were 99.30%,96.63%,and 99.14%for CCD-NPRSM,hybrid ANN-XGBoost,and CART,respectively,compared to the 98.46%RB5 removal rate observed experimentally under optimum conditions:approximately 20 mA/cm2 of current density,20 min of electrolysis time,and 65 mg/L of RB5.The optimised mineralisation efficiencies ranged between 89%and 92%for different models based on total organic carbon(TOC).Experimental studies confirmed that the predictive efficiency of optimised models ranked in the descending order of hybrid ANN-XGBoost,CCD-NPRSM,and CART.Model validation using analysis of variance(ANOVA)revealed that hybrid ANN-XGBoost had a mean squared error(MSE)and a coefficient of determination(R2)of approximately 0.014 and 0.998,respectively,for the RB5 removal efficiency,outperforming CCD-NPRSM with MSE and R2 of 0.518 and 0.998,respectively.Overall,the hybrid ANN-XGBoost approach is the most feasible technique for assessing the electrochemical treatment efficiency in RB5 dye wastewater decolourisation. 展开更多
关键词 Three-dimensional electrochemical treatment Dye-polluted wastewater Artificial intelligence Machine learning Optimisation Analysis of variance Error function analysis
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Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning 认领 引用 被引量：8
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作者 Ye‐Qun Wang Jian‐Yu Li +2 位作者 Chun‐Hua Chen Jun Zhang Zhi‐Hui Zhan 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第3期849-862,共14页
Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to ... Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost. 展开更多
关键词 deep learning evolutionary computation hyperparameter and architecture optimisation neural networks particle swarm optimisation scale‐adaptive fitness evaluation
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Optimisation of processing conditions and characterisation of structural and nutritional changes in bulgur wheat 认领 引用
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作者 Devyani Tomar Manisha Malik +1 位作者 Akash Kumar Bhupendar Singh Khatkar 《Food Materials Research》 CAS 2025年第1期88-98,共11页
Bulgur wheat is a nutritious,affordable,and staple food suitable for all socioeconomic groups.However,there are no standardised processing conditions for its production.Therefore,this study was conducted to optimise t... Bulgur wheat is a nutritious,affordable,and staple food suitable for all socioeconomic groups.However,there are no standardised processing conditions for its production.Therefore,this study was conducted to optimise the processing conditions to produce bulgur wheat by incorporating independent variables(soaking temperature,soaking time,steaming time,drying temperature,and drying time)and responses(yield,colour,cooking time,cooking loss,and hardness)which were not collectively considered in previous studies.The optimised processing conditions for soaking temperature,soaking time,steaming time,drying temperature,and drying time were 59.9°C,2 h,13.4 min,54.4°C,and 13.3 h,respectively.The characterisation of nutritional composition revealed that the loss of protein was nonsignificant(p>0.05)while the loss of fat was significant(p<0.05).Although there were losses of water-soluble vitamins and minerals,substantial levels of these micronutrients were retained in bulgur wheat.The percent degree of crystallinity decreased with the loss of the A-type diffraction pattern in bulgur wheat.The conformational peaks in the Amide I region of bulgur wheat demonstrated a decrease inβ-conformations andα-helices,and an increase in unordered structure.The study emphasises the understanding of the interrelation between processing conditions and characteristics of bulgur wheat.The desirable quality characteristics of bulgur wheat observed in the current study support the operational efficiency of the optimised processing conditions. 展开更多
关键词 optimise processing conditions bulgur wheat processing conditions nutritional composition structural changes cooking properties incorporating independent variables soaking
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Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition 认领 引用
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作者 Shouyong Jiang Jinglei Guo +1 位作者 Yong Wang Shengxiang Yang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第9期1973-1986,共14页
Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communicati... Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm. 展开更多
关键词 Bilevel decomposition evolutionary algorithm many-objective optimisation multi-objective optimisation
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Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats 认领 引用 被引量：3
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作者 Philipp Moldtmann Julian Berk +5 位作者 Shannon Ryan Andreas Klavzar Jerome Limido Christopher Lange Santu Rana Svetha Venkatesh 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第10期1-12,共12页
We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod proj... We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod projectile and surrogate shaped charge(SC)warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach,utilising a novel human-machine teaming framework for BO is also evaluated.Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations,outperforming both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples. 展开更多
关键词 Terminal ballistics Armour Explosive reactive armour Optimisation Bayesian optimisation
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3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA 认领 引用 被引量：2
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作者 K.Sreelakshmy Himanshu Gupta +3 位作者 Om Prakash Verma Kapil Kumar Abdelhamied A.Ateya Naglaa F.Soliman 《Computer Systems Science & Engineering》 SCIE EI 2023年第6期2483-2503,共21页
Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedi... Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment. 展开更多
关键词 Archimedes optimisation algorithm grey wolf optimisation path planning reinforcement learning unmanned aerial vehicles
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Multiresolution Isogeometric Topology Optimisation Using Moving Morphable Voids 认领 引用 被引量：8
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作者 Bingxiao Du Yong Zhao +2 位作者 Wen Yao Xuan Wang Senlin Huo 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第3期1119-1140,共22页
A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed t... A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs. 展开更多
关键词 Isogeometric analysis(IGA) multiresolution moving morphable voids(MMV) topology optimisation.
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Modelling-based Optimisation of the Direct Synthesis of Dimethyl Ether from Syngas in a Commercial Slurry Reactor 认领 引用 被引量：7
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作者 Sadegh Papari Mohammad Kazemeini Moslem Fattahi 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2013年第6期611-621,共11页
In the present study,we developed a multi-component one-dimensional mathematical model for simulation and optimisation of a commercial catalytic slurry reactor for the direct synthesis of dimethyl ether(DME)from synga... In the present study,we developed a multi-component one-dimensional mathematical model for simulation and optimisation of a commercial catalytic slurry reactor for the direct synthesis of dimethyl ether(DME)from syngas and CO2,operating in a churn-turbulent regime.DME productivity and CO conversion were optimised by tuning operating conditions,such as superficial gas velocity,catalyst concentration,catalyst mass over molar gas flow rate(W/F),syngas composition,pressure and temperature.Reactor modelling was accomplished utilising mass balance,global kinetic models and heterogeneous hydrodynamics.In the heterogeneous flow regime,gas was distributed into two bubble phases:small and large.Simulation results were validated using data obtained from a pilot plant.The developed model is also applicable for the design of large-scale slurry reactors. 展开更多
关键词 modelling slurry bubble column optimisation dimethyl ether synthesis two-bubble phases
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A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm 认领 引用 被引量：5
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作者 Renwu Yan Yihan Lin +1 位作者 Ning Yu Yi Wu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第1期29-39,共11页
Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electri... Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases. 展开更多
关键词 ant-lion optimisation algorithm carbon trading Levi flight low-carbon economic dispatch wind power market
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Volume Optimisation of Multi-stable Origami Bellows for Deployable Space Habitats 认领 引用 被引量：5
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作者 Mengzhu Yang Joe Defillion +1 位作者 Fabrizio Scarpa Mark Schenk 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2023年第4期514-530,共17页
Origami bellows are formed by folding flat sheets into closed cylindrical structures along predefined creases.As the bellows unfold,the volume of the origami structure will change significantly,offering potential for ... Origami bellows are formed by folding flat sheets into closed cylindrical structures along predefined creases.As the bellows unfold,the volume of the origami structure will change significantly,offering potential for use as inflatable deployable structures.This paper presents a geometric study of the volume of multi-stable Miura-ori and Kresling bellows,focusing on their application as deployable space habitats.Such habitats would be compactly stowed during launch,before expanding once in orbit.The internal volume ratio between different deployed states is investigated across the geometric design space.As a case study,the SpaceX Falcon 9 payload fairing is chosen for the transportation of space habitats.The stowed volume and effective deployed volume of the origami space habitats are calculated to enable comparison with conventional habitat designs.Optimal designs for the deployment of Miura-ori and Kresling patterned tubular space habitats are obtained using particle swarm optimisation(PSO)techniques.Configurations with significant volume expansion can be found in both patterns,with the Miura-ori patterns achieving higher volume expansion due to their additional radial deployment.A multi-objective PSO(MOPSO)is adopted to identify trade-offs between volumetric deployment and radial expansion ratios for the Miura-ori pattern. 展开更多
关键词 Optimisation Origami bellows Space habitat Origami volume
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Optimisation of laser welding parameters for welding of P92 material using Taguchi based grey relational analysis 认领 引用 被引量：3
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作者 Shanmugarajan B. Rishabh SHRIVASTAVA +1 位作者 Sathiya P. Buvanashekaran G. 《Defence Technology（防务技术）》 SCIE CAS CSCD 2016年第4期343-350,共8页
Creep strength enhanced ferritic(CSEF) steels are used in advanced power plant systems for high temperature applications. P92(Cr–W–Mo–V)steel, classified under CSEF steels, is a candidate material for piping, tubin... Creep strength enhanced ferritic(CSEF) steels are used in advanced power plant systems for high temperature applications. P92(Cr–W–Mo–V)steel, classified under CSEF steels, is a candidate material for piping, tubing, etc., in ultra-super critical and advanced ultra-super critical boiler applications. In the present work, laser welding process has been optimised for P92 material by using Taguchi based grey relational analysis(GRA).Bead on plate(BOP) trials were carried out using a 3.5 k W diffusion cooled slab CO_2 laser by varying laser power, welding speed and focal position. The optimum parameters have been derived by considering the responses such as depth of penetration, weld width and heat affected zone(HAZ) width. Analysis of variance(ANOVA) has been used to analyse the effect of different parameters on the responses. Based on ANOVA, laser power of 3 k W, welding speed of 1 m/min and focal plane at-4 mm have evolved as optimised set of parameters. The responses of the optimised parameters obtained using the GRA have been verified experimentally and found to closely correlate with the predicted value.? 2016 China Ordnance Society. Production and hosting by Elsevier B.V. All rights reserved. 展开更多
关键词 Laser Welding Optimisation Taguchi P92
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