Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provid...Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.展开更多
Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen e...Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durabili...Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.展开更多
The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for e...The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc...To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.展开更多
Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a syst...Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health.We selected representative algorithms published between 2019 and 2024,and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts.CThe Harris Hawks Optimizer(HHO)demonstrated the highest early citation impact.The study also examined applications in disease prediction models,clinical decision support,and intelligent health monitoring.Notably,the Chaotic Salp Swarm Algorithm(CSSA)achieved 99.69% accuracy in detecting Novel Coronavirus Pneumonia.Future research should progress in three directions:improving theoretical reliability and performance predictability in medical contexts;designing more adaptive and deployable mechanisms for real-world systems;and integrating ethical,privacy,and technological considerations to enable precision medicine,digital twins,and intelligent medical devices.展开更多
Optimizing the rotor pole-shoe structure of large salient pole synchronous motors is critical for improving their performance and efficiency,allowing for enhanced responsiveness to grid demands and adjustments in oper...Optimizing the rotor pole-shoe structure of large salient pole synchronous motors is critical for improving their performance and efficiency,allowing for enhanced responsiveness to grid demands and adjustments in operating conditions.This paper provides a comprehensive review of various pole-shoe structures for salient pole synchronous motor rotors and their associated optimization techniques.First,it outlines the role of the pole-shoe structure and examines the theoretical theories of key electromagnetic parameters,including the pole-arc coefficient,voltage waveform coefficient,and armature reaction coefficient.Regarding structural design,this paper explores several configurations,including the threesegment arc,five-segment arc,single eccentric pole-arc combined with two chordal surface sections,and asymmetric poles.The effects of these designs on the air-gap magnetic field distribution and voltage waveform are evaluated.In terms of methodology,this paper reviews the application of numerical solutions to electromagnetic field inverse problems and the use of optimization algorithms for electrical machine structural optimization.This study illustrates the application of improved simulated annealing algorithms,tabu search algorithms,and particle swarm optimization algorithms for single-objective optimization of five-segment arc pole-shoe structures.Additionally,this paper discusses the use of vector tabu search and multi-objective quantum evolutionary algorithms for the multi-objective optimization of five-segment arc pole-shoe structures.The study concludes that multi-objective optimization algorithms are underutilized for pole-shoe structure optimization and suggests that multi-objective particle swarm optimization could be more extensively employed for this purpose.Furthermore,the potential application of topology optimization methods for the design of salient-pole synchronous motor rotor magnetic poles is proposed.展开更多
In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirement...In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirements,respectively.However,achieving non-interfering coupling between these two optimization processes remains a challenge.An integrated layout-structure design method based on thermal metamaterials is proposed,which comprises two design stages.In the first stage,components layout optimization is conducted to maximize temperature uniformity within the satellite module,yielding a globally optimized layout with balanced thermal characteristics.In the second stage,topology optimization guided by the design principle of thermal metamaterials is implemented in critical local panel regions to satisfy differentiated heat transfer requirements of components with diverse functional and thermal sensitivity properties.The key innovation lies in utilizing thermal metamaterials as a mediator to synergistically couple global components layout optimization with local structural topology optimization,which enables customized local heat flux manipulation without interfering with the globally optimized temperature field derived from the layout optimization.The method introduces neither additional mass nor special materials,offering advantages of low cost,high reliability,and strong versatility.It provides a new solution paradigm for the design of passive thermal management systems in satellites.展开更多
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified...Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentrat...Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentration,and forming defects are prone to occur during their extrusion.This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U,multi-cavity thin-walled structure.Firstly,hot compression experiments were conducted at temperatures of 350○C,400○C,and 450○C,with strain rates of 0.01 and 1.0 s^(−1),to investigate the plastic deformation behavior of the material.Subsequently,a 3D coupled thermo-mechanical extrusion simulation model was established using Deform-3D to systematically analyze the influence of die structure and process parameters on metal flow velocity,effective stress/strain,and temperature distribution.The simulation revealed significant velocity differences,stress concentration,and uneven temperature distribution.Key parameters,including mesh density,extrusion ratio,die fillet,and bearing length,were optimized through full-factorial experiments.This optimization,combined with a stepped flow-guiding die design,effectively improved the metal flow pattern during extrusion.Trial production based on both the initial and optimized parameters were carried out.A comparative analysis demonstrates that the optimized scheme results in a final profile whose cross-section matches the target design closely,with complete filling of complex features and no obvious forming defects.This research provides a valuable reference for the extrusion process optimization and die design of complex-section profiles made from ultra-high-strength aluminum alloys.展开更多
The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streaml...The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs.展开更多
Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.T...Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization(PWO)algorithm.The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves,also known as African wild dogs in the wild,particularly their unique consensus-based voting rally mechanism,a behavior fundamentally distinct fromthe social dynamics of grey wolves.In this innovative process,pack members explore different areas to find prey;then,they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey.The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions,including the Congress on Evolutionary Computation(CEC)2017 suite and different real-world engineering design cases.Furthermore,the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces.This includes its application within the field of cybersecurity,specifically in the context of training a machine learning-based intrusion detection system(ML-IDS),achieving an accuracy of 0.90 and an F-measure of 0.9290.Statistical analyses using the Wilcoxon signed-rank test(all p<0.05)indicate that the PWO algorithm outperforms existing state-of-the-art algorithms,providing superior solutions in diverse and unpredictable optimization landscapes.This demonstrates its potential as a robust method for tackling complex optimization problems in various fields.The source code for thePWOalgorithmis publicly available at https://github.com/saeidsheikhi/Painted-Wolf-Optimization.展开更多
Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(...Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.展开更多
Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drill...Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drilling extension limits,underground target distribution and trajectory collision risks,a model of platform location-wellbore trajectory collaborative optimization for a complex-structure well factory is developed.A hybrid heuristic algorithm is proposed by combining an improved sparrow search algorithm(ISSA)for optimizing platform parameters in the outer layer and a directed artificial bee colony algorithm(DABC)for optimizing trajectory parameters in the inner layer.The alternating iteration of ISSA-DABC facilitates the resolution of the collaborative optimization problem.The ISSA-DABC provides an effective solution to the platform-trajectory collaborative optimization problem for complex-structure well factories and overcomes the tendency of the traditional platform-trajectory stepwise optimization workflow to become trapped in local optima and yield inconsistent designs.The ISSA-DABC has a strong global search capability,fast convergence and good robustness,and can simultaneously satisfy multiple engineering constraints on drilling footage,trajectory complexity and collision risk,and enables automated,workflow-wide generation of constraint-compliant,near-globally optimal platform-trajectory configurations.Field applications further demonstrate that ISSA-DABC significantly reduces the objective function value and collision risk,yielding more rational platform layouts and well factory design parameters.展开更多
Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru...Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.展开更多
The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation h...The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation has grown more vibrant,thus a novel approach called safe deep reinforcement learning is proposed in this paper.Herein,the real-time ACOPF problem is modeled as a constrained Markov decision process,and primal-dual optimization(PDO)based proximal policy optimization(PPO)is used to learn the optimal generator outputs in the primal domain and security constraints in the dual domain,which avoids manually selecting a trade-off between penalties for constraint violations and rewards for the economy.Before training,behavior cloning clones the expert experience into the initial weights of neural networks.Moreover,multiprocessing training is utilized to accelerate the training speed.Case studies are conducted on the IEEE 118-bus system and the modified IEEE 118-bus system.Compared with other methods,the experimental results show that the proposed method can achieve security and near-optimal economic goals by fast calculating the real-time ACOPF problem.展开更多
基金Support by National Natural Science Foundation of China(22127802,22573091)the HY Action(62402010305)。
文摘Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.
基金Supported by the National Natural Science Foundation of China(No.52273056)the Science and Technology Development Program of Jilin Province,China(No.YDZJ202501ZYTS305)。
文摘Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金National MCF Energy R&D Program(2024YFE03260300)。
文摘Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.
文摘The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
文摘To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.
基金Supported by National Natural Science Foundation of China(Grant No.62506054)Natural Science Foundation of Chongqing,China(Grant Nos.CSTB2022NSCQ-MSX1571,CSTB2024NSCQ-MSX1118)+2 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant Nos.KJQN202400841,KJZD-M202500804)The National Natural Science Foundation of China(Grant No.61976030)Chongqing Technology and Business University High-level Talent Research Initiation Project(Grant No.2256004).
文摘Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health.We selected representative algorithms published between 2019 and 2024,and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts.CThe Harris Hawks Optimizer(HHO)demonstrated the highest early citation impact.The study also examined applications in disease prediction models,clinical decision support,and intelligent health monitoring.Notably,the Chaotic Salp Swarm Algorithm(CSSA)achieved 99.69% accuracy in detecting Novel Coronavirus Pneumonia.Future research should progress in three directions:improving theoretical reliability and performance predictability in medical contexts;designing more adaptive and deployable mechanisms for real-world systems;and integrating ethical,privacy,and technological considerations to enable precision medicine,digital twins,and intelligent medical devices.
文摘Optimizing the rotor pole-shoe structure of large salient pole synchronous motors is critical for improving their performance and efficiency,allowing for enhanced responsiveness to grid demands and adjustments in operating conditions.This paper provides a comprehensive review of various pole-shoe structures for salient pole synchronous motor rotors and their associated optimization techniques.First,it outlines the role of the pole-shoe structure and examines the theoretical theories of key electromagnetic parameters,including the pole-arc coefficient,voltage waveform coefficient,and armature reaction coefficient.Regarding structural design,this paper explores several configurations,including the threesegment arc,five-segment arc,single eccentric pole-arc combined with two chordal surface sections,and asymmetric poles.The effects of these designs on the air-gap magnetic field distribution and voltage waveform are evaluated.In terms of methodology,this paper reviews the application of numerical solutions to electromagnetic field inverse problems and the use of optimization algorithms for electrical machine structural optimization.This study illustrates the application of improved simulated annealing algorithms,tabu search algorithms,and particle swarm optimization algorithms for single-objective optimization of five-segment arc pole-shoe structures.Additionally,this paper discusses the use of vector tabu search and multi-objective quantum evolutionary algorithms for the multi-objective optimization of five-segment arc pole-shoe structures.The study concludes that multi-objective optimization algorithms are underutilized for pole-shoe structure optimization and suggests that multi-objective particle swarm optimization could be more extensively employed for this purpose.Furthermore,the potential application of topology optimization methods for the design of salient-pole synchronous motor rotor magnetic poles is proposed.
基金funded by State Key Laboratory of MicroSpacecraft Rapid Design and Intelligent Cluster,China(No.MS01240104)the Youth Program of the Self-Innovation Science Fund,China(No.ZK2023-41)from the National University of Defense Technology(NUDT)China and the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX20240155)。
文摘In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirements,respectively.However,achieving non-interfering coupling between these two optimization processes remains a challenge.An integrated layout-structure design method based on thermal metamaterials is proposed,which comprises two design stages.In the first stage,components layout optimization is conducted to maximize temperature uniformity within the satellite module,yielding a globally optimized layout with balanced thermal characteristics.In the second stage,topology optimization guided by the design principle of thermal metamaterials is implemented in critical local panel regions to satisfy differentiated heat transfer requirements of components with diverse functional and thermal sensitivity properties.The key innovation lies in utilizing thermal metamaterials as a mediator to synergistically couple global components layout optimization with local structural topology optimization,which enables customized local heat flux manipulation without interfering with the globally optimized temperature field derived from the layout optimization.The method introduces neither additional mass nor special materials,offering advantages of low cost,high reliability,and strong versatility.It provides a new solution paradigm for the design of passive thermal management systems in satellites.
文摘Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB3710805).
文摘Ultra-high-strength aluminumalloy profile is an ideal choice for aerospace structuralmaterials due to its excellent specific strength and corrosion resistance.However,issues such as uneven metal flow,stress concentration,and forming defects are prone to occur during their extrusion.This study focuses on an Al-Zn-Mg-Cu ultra-high-strength aluminum alloy profile with a double-U,multi-cavity thin-walled structure.Firstly,hot compression experiments were conducted at temperatures of 350○C,400○C,and 450○C,with strain rates of 0.01 and 1.0 s^(−1),to investigate the plastic deformation behavior of the material.Subsequently,a 3D coupled thermo-mechanical extrusion simulation model was established using Deform-3D to systematically analyze the influence of die structure and process parameters on metal flow velocity,effective stress/strain,and temperature distribution.The simulation revealed significant velocity differences,stress concentration,and uneven temperature distribution.Key parameters,including mesh density,extrusion ratio,die fillet,and bearing length,were optimized through full-factorial experiments.This optimization,combined with a stepped flow-guiding die design,effectively improved the metal flow pattern during extrusion.Trial production based on both the initial and optimized parameters were carried out.A comparative analysis demonstrates that the optimized scheme results in a final profile whose cross-section matches the target design closely,with complete filling of complex features and no obvious forming defects.This research provides a valuable reference for the extrusion process optimization and die design of complex-section profiles made from ultra-high-strength aluminum alloys.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under grant number 114-2221-E-182-041-MY3by Chang Gung University and Chang Gung Memorial Hospital under project number NERPD4Q0021.
文摘The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs.
文摘Metaheuristic optimization algorithms continue to be essential for solving complex real-world problems,yet existingmethods often struggle with balancing exploration and exploitation across diverse problem landscapes.This paper proposes a novel nature-inspired metaheuristic optimization algorithm named the Painted Wolf Optimization(PWO)algorithm.The main inspiration for the PWO algorithm is the group behavior and hunting strategy of painted wolves,also known as African wild dogs in the wild,particularly their unique consensus-based voting rally mechanism,a behavior fundamentally distinct fromthe social dynamics of grey wolves.In this innovative process,pack members explore different areas to find prey;then,they hold a pre-hunting voting rally based on the alpha member to determine who will begin the hunt and attack the prey.The efficiency of the proposed PWO algorithm is evaluated by a comparison study with other well-known optimization algorithms on 33 test functions,including the Congress on Evolutionary Computation(CEC)2017 suite and different real-world engineering design cases.Furthermore,the algorithm’s performance is further tested across a spectrum of optimization problems with extensive unknown search spaces.This includes its application within the field of cybersecurity,specifically in the context of training a machine learning-based intrusion detection system(ML-IDS),achieving an accuracy of 0.90 and an F-measure of 0.9290.Statistical analyses using the Wilcoxon signed-rank test(all p<0.05)indicate that the PWO algorithm outperforms existing state-of-the-art algorithms,providing superior solutions in diverse and unpredictable optimization landscapes.This demonstrates its potential as a robust method for tackling complex optimization problems in various fields.The source code for thePWOalgorithmis publicly available at https://github.com/saeidsheikhi/Painted-Wolf-Optimization.
基金supported by the National Natural Science Foundation of China(Grant No.12402139,No.52368070)supported by Hainan Provincial Natural Science Foundation of China(Grant No.524QN223)+3 种基金Scientific Research Startup Foundation of Hainan University(Grant No.RZ2300002710)State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology(Grant No.GZ24107)the Horizontal Research Project(Grant No.HD-KYH-2024022)Innovative Research Projects for Postgraduate Students in Hainan Province(Grant No.Hys2025-217).
文摘Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.
基金Supported by Key Program of Natural Science Foundation of China(52234002)Major Program Project of the National Natural Science Foundation of China(52394255)。
文摘Using platform-target matching deviation,anti-collision difficulty,trajectory complexity,and total drilling footage as objective functions,and comprehensively considering constraints such as platform layout area,drilling extension limits,underground target distribution and trajectory collision risks,a model of platform location-wellbore trajectory collaborative optimization for a complex-structure well factory is developed.A hybrid heuristic algorithm is proposed by combining an improved sparrow search algorithm(ISSA)for optimizing platform parameters in the outer layer and a directed artificial bee colony algorithm(DABC)for optimizing trajectory parameters in the inner layer.The alternating iteration of ISSA-DABC facilitates the resolution of the collaborative optimization problem.The ISSA-DABC provides an effective solution to the platform-trajectory collaborative optimization problem for complex-structure well factories and overcomes the tendency of the traditional platform-trajectory stepwise optimization workflow to become trapped in local optima and yield inconsistent designs.The ISSA-DABC has a strong global search capability,fast convergence and good robustness,and can simultaneously satisfy multiple engineering constraints on drilling footage,trajectory complexity and collision risk,and enables automated,workflow-wide generation of constraint-compliant,near-globally optimal platform-trajectory configurations.Field applications further demonstrate that ISSA-DABC significantly reduces the objective function value and collision risk,yielding more rational platform layouts and well factory design parameters.
文摘Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.
基金supported by the National Natural Science Foundation of China(52007173 and U22B2098).
文摘The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation has grown more vibrant,thus a novel approach called safe deep reinforcement learning is proposed in this paper.Herein,the real-time ACOPF problem is modeled as a constrained Markov decision process,and primal-dual optimization(PDO)based proximal policy optimization(PPO)is used to learn the optimal generator outputs in the primal domain and security constraints in the dual domain,which avoids manually selecting a trade-off between penalties for constraint violations and rewards for the economy.Before training,behavior cloning clones the expert experience into the initial weights of neural networks.Moreover,multiprocessing training is utilized to accelerate the training speed.Case studies are conducted on the IEEE 118-bus system and the modified IEEE 118-bus system.Compared with other methods,the experimental results show that the proposed method can achieve security and near-optimal economic goals by fast calculating the real-time ACOPF problem.
