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
Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a nove...Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions.First,the equations of motion for the aircraft are reformulated as a system of stochastic differential equations,which are subsequently transformed into a deterministic form.By modeling turbulence as a Gaussian random process and incorporating it directly into the control system,the proposed method proactively compensates for the adverse effects of turbulence.The transformation is achieved using semi-invariant techniques.Second,the control problem is formulated as an optimization task,aiming to minimize the deviation between the actual and desired turn characteristics,specifically the angular velocity.Finally,a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters.Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches,ensuring both stability and precision in turbulent wind conditions.展开更多
In this paper,we study a class of Linear Fractional Programming on a nonempty bounded set,called the Problem(LFP),and design a branch and bound algorithm to find the global optimal solution of the problem(LFP).First,w...In this paper,we study a class of Linear Fractional Programming on a nonempty bounded set,called the Problem(LFP),and design a branch and bound algorithm to find the global optimal solution of the problem(LFP).First,we convert the problem(LFP)to the equivalent problem(EP2).Secondly,by applying the linear relaxation technique to the problem(EP2),the linear relaxation programming problem(LRP2Y)was obtained.Then,the overall framework of the algorithm is given,and the convergence and complexity of the algorithm are analyzed.Finally,experimental results are listed to illustrate the effectiveness of the algorithm.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those co...Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations.展开更多
Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and ...Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality.We propose a drift-aware,power-quality-constrained MPPT framework that co-optimizes MPPT,PLL,and current-loop gains under stochastic frequency drift,while enforcing IEEE-519 limits(per-order Ih/IL and TDD)during optimization.Unlike energy-only or THD-only methods,the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting.Operating scenarios are calibrated to Cameroon’s Southern Interconnected Grid and city-specific profiles(Douala/Yaoundé),combining measured-style irradiance/temperature traces,partial-shading patterns,and stochastic frequency drift up to±0.8 Hz with synthetic contingencies.Across a 30-scenario campaign,the proposed controller achievesηMPPT=99.3%–99.6%(vs.98.6%Incremental Conductance and 97.8%Perturb-and-Observe),lowers DC-link ripple by 35%–48%,reduces oscillatory PCC power by≈41%,maintains THD≤2.5%(5%limit)and PF≥0.99,and shortens irradiance-step settling from 85–110 ms to 50–65 ms.Sensitivity to PLL bandwidth shows a broad optimum(≈60–90 Hz)with minimum THD/ripple,and ablations confirm that explicit drift weighting is pivotal to ripple and THD suppression without sacrificing yield.The approach is controller-agnostic,firmware-deployable,and generalizes to other converter-interfaced renewables;we outline a short hardware-/HIL-validation path for adoption in Sub-Saharan grids.展开更多
Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challen...Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.展开更多
Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive ...Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems.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
In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence...In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence,asymptotic behavior and blow-up of solutions with initial energy J(u_(0))≤d.Moreover,we estimate the upper bound of the blow-up time for J(u_(0))≤0.展开更多
The Global Governance Initiative(GGI)is another important public good that China has provided for the international community,following the Global Development Initiative,the Global Security Initiative,and the Global C...The Global Governance Initiative(GGI)is another important public good that China has provided for the international community,following the Global Development Initiative,the Global Security Initiative,and the Global Civilization Initiative.As a sub-field of global governance,global climate governance has hitherto progressed slowly,exhibiting growing deficits in discourse,institutions,and actions.The governance paradigm is in desperate need of revamping,especially under the guidance of an innovative initiative.At the conceptual level,the GGI has gone beyond the Western governance discourse by outlining the institutional starting point,foundations of legitimacy,action orientations,ultimate goals,and practical support for good global climate governance through its five core concepts.At the practical level,the spillover effects of China's achievements in climate governance and the collective rise of the Global South have created favorable conditions,domestically and internationally,for the GGI to lead the way forward toward good global climate governance.However,the fact that the international political and economic order remains unjust and inequitable indicates that this will inevitably be a long-term process replete with rivalries in the pursuit of breakthroughs.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
In recent years, the overall strength of Western developed countries, led by the United States, has declined relatively, resulting in a trend of decentralization. This has posed severe challenges to the traditional gl...In recent years, the overall strength of Western developed countries, led by the United States, has declined relatively, resulting in a trend of decentralization. This has posed severe challenges to the traditional global governance system with regard to trade, security, and multilateral cooperation. However, this trend also provides a historical opportunity to transform the global governance system into one that is more inclusive and equitable. This can be seen in the reconstruction of value priorities, the adjustment of governance focus, the reform of governance mechanisms, and the promotion of new mechanisms. Only by adhering to openness and inclusiveness and balancing rights and responsibilities can we build a future governance order that conforms to the common interests of all humanity.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
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
Multiple diagnoses,from both East and West,converge on the view that we are living through a turbulent juncture reminiscent of the inflection points that preceded major wars,not as a claim of historical symmetry,but a...Multiple diagnoses,from both East and West,converge on the view that we are living through a turbulent juncture reminiscent of the inflection points that preceded major wars,not as a claim of historical symmetry,but as an indication of the depth of the present crisis.What concerns us here is to affirm the essence of that assessment:we are indeed facing an unprecedented crisis that compels a rethinking of the entire existing international order,as U.S.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
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
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co-supported by the Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province(No.22kftk01)the Key Research and Development Program of Heilongjiang,China(No.2024ZXJ07B05)the National Natural Science Foundation of China(No.92471103)。
文摘Unmanned aircraft are highly vulnerable to crosswind-induced turbulence during complex maneuvers such as turning,which can significantly compromise control and reduce autopilot effectiveness.This paper presents a novel control strategy to improve the controllability of unmanned aircraft in challenging wind conditions.First,the equations of motion for the aircraft are reformulated as a system of stochastic differential equations,which are subsequently transformed into a deterministic form.By modeling turbulence as a Gaussian random process and incorporating it directly into the control system,the proposed method proactively compensates for the adverse effects of turbulence.The transformation is achieved using semi-invariant techniques.Second,the control problem is formulated as an optimization task,aiming to minimize the deviation between the actual and desired turn characteristics,specifically the angular velocity.Finally,a new numerical method with proven global convergence is employed to compute the optimal autopilot parameters.Simulation results using a medium-range unmanned aircraft model under continuous turbulent gusts demonstrate that the proposed method significantly outperforms existing approaches,ensuring both stability and precision in turbulent wind conditions.
基金Supported by the National Natural Science Foundation of China(Grant Nos.12571317 and 12071133).
文摘In this paper,we study a class of Linear Fractional Programming on a nonempty bounded set,called the Problem(LFP),and design a branch and bound algorithm to find the global optimal solution of the problem(LFP).First,we convert the problem(LFP)to the equivalent problem(EP2).Secondly,by applying the linear relaxation technique to the problem(EP2),the linear relaxation programming problem(LRP2Y)was obtained.Then,the overall framework of the algorithm is given,and the convergence and complexity of the algorithm are analyzed.Finally,experimental results are listed to illustrate the effectiveness of the algorithm.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
文摘Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations.
基金Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project(Grant No.RGP2/587/46).
文摘Photovoltaic(PV)systems in the field operate under complex,uncertain conditions rapid irradiance ramps,partial shading,temperature swings,surface soiling,and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality.We propose a drift-aware,power-quality-constrained MPPT framework that co-optimizes MPPT,PLL,and current-loop gains under stochastic frequency drift,while enforcing IEEE-519 limits(per-order Ih/IL and TDD)during optimization.Unlike energy-only or THD-only methods,the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting.Operating scenarios are calibrated to Cameroon’s Southern Interconnected Grid and city-specific profiles(Douala/Yaoundé),combining measured-style irradiance/temperature traces,partial-shading patterns,and stochastic frequency drift up to±0.8 Hz with synthetic contingencies.Across a 30-scenario campaign,the proposed controller achievesηMPPT=99.3%–99.6%(vs.98.6%Incremental Conductance and 97.8%Perturb-and-Observe),lowers DC-link ripple by 35%–48%,reduces oscillatory PCC power by≈41%,maintains THD≤2.5%(5%limit)and PF≥0.99,and shortens irradiance-step settling from 85–110 ms to 50–65 ms.Sensitivity to PLL bandwidth shows a broad optimum(≈60–90 Hz)with minimum THD/ripple,and ablations confirm that explicit drift weighting is pivotal to ripple and THD suppression without sacrificing yield.The approach is controller-agnostic,firmware-deployable,and generalizes to other converter-interfaced renewables;we outline a short hardware-/HIL-validation path for adoption in Sub-Saharan grids.
基金funded by National Natural Science Foundation of China(grant No.52405255)Special Program of Huzhou(grant No.2023GZ05)+1 种基金Projects of Huzhou Science and Technology Correspondent(grant No.2023KT76)Guangdong Basic and Applied Basic Research Foundation(grant No.2025A1515010487)。
文摘Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number:IMSIU-DDRSP2503)。
文摘Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems.
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Supported by NSFC(No.12101482)the Natural Science Foundation of Shaanxi Province,China(No.2018JQ1052)。
文摘In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence,asymptotic behavior and blow-up of solutions with initial energy J(u_(0))≤d.Moreover,we estimate the upper bound of the blow-up time for J(u_(0))≤0.
基金2024 Shaanxi Provincial Social Science Fund Annual Project titled"Research on Chinese Modernization from the Perspective of Overseas Studies on the Communist Party of China"(Project Number:2024B002)a 2024 key project of Xi'an Jiaotong University for studying and expounding Xi Jinping Thought on Culture titled"Research on the World Implications of a New Model for Human Advancement"(Project Number:SKZX2024003)funded by the"Top Young Talents Project"(TZ0275)of Shaanxi Province's second batch of"Special Support Program for High-Level Talents"(Philosophy,Social Sciences,Culture,and Arts).
文摘The Global Governance Initiative(GGI)is another important public good that China has provided for the international community,following the Global Development Initiative,the Global Security Initiative,and the Global Civilization Initiative.As a sub-field of global governance,global climate governance has hitherto progressed slowly,exhibiting growing deficits in discourse,institutions,and actions.The governance paradigm is in desperate need of revamping,especially under the guidance of an innovative initiative.At the conceptual level,the GGI has gone beyond the Western governance discourse by outlining the institutional starting point,foundations of legitimacy,action orientations,ultimate goals,and practical support for good global climate governance through its five core concepts.At the practical level,the spillover effects of China's achievements in climate governance and the collective rise of the Global South have created favorable conditions,domestically and internationally,for the GGI to lead the way forward toward good global climate governance.However,the fact that the international political and economic order remains unjust and inequitable indicates that this will inevitably be a long-term process replete with rivalries in the pursuit of breakthroughs.
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In recent years, the overall strength of Western developed countries, led by the United States, has declined relatively, resulting in a trend of decentralization. This has posed severe challenges to the traditional global governance system with regard to trade, security, and multilateral cooperation. However, this trend also provides a historical opportunity to transform the global governance system into one that is more inclusive and equitable. This can be seen in the reconstruction of value priorities, the adjustment of governance focus, the reform of governance mechanisms, and the promotion of new mechanisms. Only by adhering to openness and inclusiveness and balancing rights and responsibilities can we build a future governance order that conforms to the common interests of all humanity.
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Multiple diagnoses,from both East and West,converge on the view that we are living through a turbulent juncture reminiscent of the inflection points that preceded major wars,not as a claim of historical symmetry,but as an indication of the depth of the present crisis.What concerns us here is to affirm the essence of that assessment:we are indeed facing an unprecedented crisis that compels a rethinking of the entire existing international order,as U.S.
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.
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.
