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Structural optimization of stress-bearing structures of nearly incompressible problems under design-dependent pressure loads 认领 引用 被引量：1
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作者 T.T.BANH N.T.Y.NGUYEN +1 位作者 H.P.BAN D.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第4期905-926,共22页
An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite fini... An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite finite element approach,the volumetric locking in nearly incompressible materials is reduced.A fluid-flow-based model is built,in which a design-dependent pressure variable is introduced to capture the loading conditions within the system.The P-norm approach consolidates the stress metrics into a global measure,while the clustered regional scaling and adaptive techniques enhance the solutions for stress-limited cases.The primary contributions of this work include a novel framework for addressing the stress challenges in coupled mechanical-pressure systems via flow-based modeling,the adaptability to both compressible and nearly incompressible materials,and the compatibility with diverse mesh types,including triangular,quadrilateral,and polygonal elements.The numerical examples demonstrate,for the first time,optimized topologies for nearly incompressible materials under stress constraints in coupled mechanical-pressure environments,emphasizing the unique strength of this approach. 展开更多
关键词 topology optimization(TO) stress-related problem design-dependent load near incompressibility mechanical-pressure system polytopal composite finite element
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Cooperative Metaheuristics with Dynamic Dimension Reduction for High-Dimensional Optimization Problems 认领 引用
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作者 Junxiang Li Zhipeng Dong +2 位作者 Ben Han Jianqiao Chen Xinxin Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1484-1502,共19页
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta... Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems. 展开更多
关键词 Dimension reduction modified principal components analysis high-dimensional optimization problems cooperative metaheuristics metaheuristic algorithms
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A high-performance parallel algorithm based on problem independent machine learning(PIML)for large-scale topology optimization 认领 引用
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作者 Xinyu Ma Mengcheng Huang +4 位作者 Zongliang Du Yilin Guo Chang Liu Yue Mei Xu Guo 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期197-213,共17页
Although supplying extensive design space,the curse of dimensionality restricts the widespread application of largescale topology optimization in practical engineering.Various acceleration techniques have been integra... Although supplying extensive design space,the curse of dimensionality restricts the widespread application of largescale topology optimization in practical engineering.Various acceleration techniques have been integrated with topology optimization,achieving significant attention and progress in large-scale problems.This work aims to investigate how much benefit can be obtained by combining parallel computing and machine learning techniques to enhance the efficiency of large-scale topology optimization algorithms.Accordingly,a parallel problem independent machine learning(PIML)-enhanced topology optimization method is proposed.The PIML model substantially reduces the dimension of the condensed stiffness matrix and its computational cost,and parallel computing reduces the workload per process and enables the application of a parallel multigrid solver.Besides,several techniques,such as matrix-free implementation,direct condensation of uniform coarse elements,and adjusting computational resource limits,have been developed to enhance computational efficiency.The weak scaling efficiency,strong scaling speedup,and maximum achievable efficiency of the proposed method are validated across multiple numerical examples,showing significant improvement in the tractable problem size and solution efficiency compared to traditional topology optimization algorithms. 展开更多
关键词 Topology optimization Large-scale Problem independent machine learning(PIML) Parallel computing
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An Overall Optimization Model Using Metaheuristic Algorithms for the CNN-Based IoT Attack Detection Problem 认领 引用 被引量：1
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作者 Le Thi Hong Van Le Duc Thuan +1 位作者 Pham Van Huong Nguyen Hieu Minh 《Computers, Materials & Continua》 SCIE EI 2026年第4期1934-1964,共31页
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
关键词 Genetic algorithm(GA) particle swarm optimization(PSO) multi-objective optimization convolutional neural network—CNN IoT attack detection metaheuristic optimization CNN configuration
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Enhanced multiscale quantum approximate optimization algorithm in multibody combinatorial optimization problems 认领 引用
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作者 Lei-Lei Chen Ping Zou Ya-Fei Yu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期424-434,共11页
At present,the quantum approximate optimization algorithm(QAOA)faces scalability challenges in high-dimensional combinatorial optimization problems due to exponentially growing computational costs and reachability def... At present,the quantum approximate optimization algorithm(QAOA)faces scalability challenges in high-dimensional combinatorial optimization problems due to exponentially growing computational costs and reachability deficits for noisy intermediate-scale quantum(NISQ)devices.This study focuses on the multiscale quantum approximate optimization algorithm(MQAOA),which integrates renormalization group(RG)transformations with QAOA to address these limitations.Based on the connections between the variables in the problem to be solved,the weighted maximal matching method is employed to generate a variable partitioning strategy guiding the RG transformation.This approach not only extends the applicability of MQAOA to satisfiability(SAT)problems—including those with three-body and higher-order interactions in the problem Hamiltonian—but also eliminates the algorithm's sensitivity to problem density.Validations conducted on quantum simulators show that,after running two-round MQAOA,its capability is enhanced to identify optimal solutions with approximately 97%success probability as defined by the ground-state overlap for Max-2-SAT problems(78%success probability for Max-3-SAT problems).The results confirm the feasibility of MQAOA and establish it as a resource-efficient framework for complex combinatorial optimization problems,providing a pathway for NISQ-era deployment. 展开更多
关键词 quantum algorithm parameterized quantum circuit renormalization group transformation combinatorial optimization Max-SAT problems
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An Improved Variant of Multi-Population Cooperative Constrained Multi-Objective Optimization(MCCMO)for Multi-Objective Optimization Problem 认领 引用
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作者 Muhammad Waqar Khan Adnan Ahmed Siddiqui Syed Sajjad Hussain Rizvi 《Computers, Materials & Continua》 SCIE EI 2026年第2期1874-1888,共15页
The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant... The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization(MCCMO)Algorithm,termed Adaptive Diversity Preservation(ADP).This enhancement is primarily focused on the improvement of constraint handling strategies,local search integration,hybrid selection approaches,and adaptive parameter control.Theimproved variant was experimented on with the RWMOP50 power distribution systemplanning benchmark.As per the findings,the improved variant outperformed the original MCCMO across the eleven performance metrics,particularly in terms of convergence speed,constraint handling efficiency,and solution diversity.The results also establish that MCCMOADP consistently delivers substantial performance gains over the baseline MCCMO,demonstrating its effectiveness across performancemetrics.The new variant also excels atmaintaining the balanced trade-off between exploration and exploitation throughout the search process,making it especially suitable for complex optimization problems in multiconstrained power systems.These enhancements make MCCMO-ADP a valuable and promising candidate for handling problems such as renewable energy scheduling,logistics planning,and power system optimization.Future work will benchmark the MCCMO-ADP against widely recognized algorithms such as NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D and will also extend its validation to large-scale real-world optimization domains to further consolidate its generalizability. 展开更多
关键词 MCCMO algorithms adaptive diversity preservation RWMOP50 power distribution system multi-modal multi objective optimization evolutionary algorithm multi objective problem
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Solving high-dimensional global optimization problems via solution space restructuring with neural network 认领 引用
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作者 N.VO T.LE-DUC +3 位作者 H.TANG H.NGUYEN-XUAN S.H.LEE J.H.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第6期1383-1400,共18页
It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-fre... It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-free techniques.However,the general framework for adaptively dealing with a particular optimization problem is commonly overlooked and thus still hidden in the literature.To overcome this limitation,a new approach assisted by the neural network(NN)is proposed for solving high-dimensional optimization issues.By restructuring the search space to optimize the objective function via a nonlinear mapping constructed by an autoencoder(AE),the surrogate solution space is constructed by a network training process and dynamically oriented to the optimal solution of the optimization issue.To enhance the optimization efficiency and address non-smooth problems,the classical metaheuristic grey wolf optimizer(GWO)and the adaptive moment estimation(Adam)are sequentially employed to complement the disadvantages of the constituted models.The effectiveness of the proposed approach is validated by solving a set of mathematical functions with 1000-dimensional and three large-scale truss design optimization problems.Several numerical experiments show that the solution space is reduced in terms of both size and complexity based on the restructuring procedure,in which the global optimal solution is still conserved,leading to better optimization efficiency when solving optimization problems with complex search domains with large dimensions.In addition,the hybrid optimizer has also been proven to be more effective when combined with the restructuring technique owing to the use of the Adam algorithm in the second phase. 展开更多
关键词 high-dimensional optimization solution space restructuring adaptive moment estimation(Adam) grey wolf optimizer(GWO) neural network(NN)
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Several Improved Models of the Mountain Gazelle Optimizer for Solving Optimization Problems 认领 引用
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作者 Farhad Soleimanian Gharehchopogh Keyvan Fattahi Rishakan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期727-780,共54页
Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characte... Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships.The Mountain Gazelle Optimizer(MGO)is notably effective but struggles to balance local search refinement and global space exploration,often leading to premature convergence and entrapment in local optima.This paper presents the Improved MGO(IMGO),which integrates three synergistic enhancements:dynamic chaos mapping using piecewise chaotic sequences to boost explo-ration diversity;Opposition-Based Learning(OBL)with adaptive,diversity-driven activation to speed up convergence;and structural refinements to the position update mechanisms to enhance exploitation.The IMGO underwent a comprehensive evaluation using 52 standardised benchmark functions and seven engineering optimization problems.Benchmark evaluations showed that IMGO achieved the highest rank in best solution quality for 31 functions,the highest rank in mean performance for 18 functions,and the highest rank in worst-case performance for 14 functions among 11 competing algorithms.Statistical validation using Wilcoxon signed-rank tests confirmed that IMGO outperformed individual competitors across 16 to 50 functions,depending on the algorithm.At the same time,Friedman ranking analysis placed IMGO with an average rank of 4.15,compared to the baseline MGO’s 4.38,establishing the best overall performance.The evaluation of engineering problems revealed consistent improvements,including an optimal cost of 1.6896 for the welded beam design vs.MGO’s 1.7249,a minimum cost of 5885.33 for the pressure vessel design vs.MGO’s 6300,and a minimum weight of 2964.52 kg for the speed reducer design vs.MGO’s 2990.00 kg.Ablation studies identified OBL as the strongest individual contributor,whereas complete integration achieved superior performance through synergistic interactions among components.Computational complexity analysis established an O(T×N×5×f(P))time complexity,representing a 1.25×increase in fitness evaluation relative to the baseline MGO,validating the favorable accuracy-efficiency trade-offs for practical optimization applications. 展开更多
关键词 Metaheuristic algorithm dynamical chaos integration opposition-based learning mountain gazelle optimizer optimization
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Auxiliary-qubit-free quantum approximate optimization algorithm for the minimum dominating set problem 认领 引用
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作者 Guanghui Li Xiaohui Ni +5 位作者 Junjian Su Sujuan Qin Fenzhuo Guo Bingjie Xu Wei Huang Fei Gao 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期326-344,共19页
Quantum approximate optimization algorithm(QAOA)is a promising framework for solving combinatorial optimization problems on near-term quantum devices.One such problem is the minimum dominating set(MDS),which is known ... Quantum approximate optimization algorithm(QAOA)is a promising framework for solving combinatorial optimization problems on near-term quantum devices.One such problem is the minimum dominating set(MDS),which is known to be NP-hard.Existing QAOA algorithms for this problem typically require numerous auxiliary qubits,increasing circuit overhead and hardware requirements.In this paper,we propose an auxiliary-qubit-free QAOA algorithm based on Hamiltonian evolution(AQFH-QAOA)for the MDS problem.Unlike previous studies that require numerous auxiliary qubits,our algorithm eliminates the need for auxiliary qubits,thereby significantly reducing circuit overhead.In addition,we present an auxiliary-qubit-free optimized implementation of the previously proposed Guerrero's QAOA algorithm(AQFG-QAOA)by utilizing gate decomposition techniques.Through a detailed analysis of gate complexity,we evaluate the applicability of these two algorithms.Numerical experiments demonstrate that our proposed algorithm achieves competitive solution quality compared with existing QAOA algorithms,making it a promising candidate for implementation on near-term quantum devices. 展开更多
关键词 quantum algorithm quantum approximate optimization algorithm minimum dominating set Boolean algebra identities
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SEMI-INFINITE INTERVAL-VALUED OPTIMIZATION PROBLEMS WITH ROBUST CONSTRAINTS 认领 引用
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作者 Anurag JAYSWAL Ajeet KUMAR 《Acta Mathematica Scientia》 SCIE CSCD 2026年第1期383-406,共24页
In this paper,we consider a robust semi-infinite interval-valued optimization problem with inequality constraints having an uncertain parameter.The parametric representation of the aforesaid problem is also considered... In this paper,we consider a robust semi-infinite interval-valued optimization problem with inequality constraints having an uncertain parameter.The parametric representation of the aforesaid problem is also considered in order to derive the necessary and sufficient optimality conditions.Furthermore,we formulate a mixed-type dual problem and derive duality results which associate the robust weak efficient solution of the primal and its dual problems.Several examples are given to illustrate the results in the manuscript. 展开更多
关键词 semi-infinite programming interval-valued programming robust weak efficient solution optimality conditions duality
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Painted Wolf Optimization:A Novel Nature-Inspired Metaheuristic Algorithm for Real-World Optimization Problems 认领 引用
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作者 Saeid Sheikhi 《Computers, Materials & Continua》 SCIE EI 2026年第5期243-271,共29页
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 http://gffzz188fe103f8f1460asxbkn59cvfwuf66cq.ffgz.tsg.suse.edu.cn/saeidsheikhi/Painted-Wolf-Optimization. 展开更多
关键词 Optimization painted wolf optimization algorithm metaheuristic algorithm nature-inspired computing swarm intelligence
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AN ACCELERATED PRECONDITIONED PRIMAL-DUAL GRADIENT ALGORITHM FOR NONCONVEX COMPOSITE OPTIMIZATION PROBLEMS WITH APPLICATIONS 认领 引用
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作者 Xian-Jun Long Jia-Lin Nie +1 位作者 Gao-Xi Li Zai-Yun Peng 《Journal of Computational Mathematics》 SCIE CSCD 2026年第6期1867-1889,共23页
In this paper,we consider a class of three-composite nonconvex optimization problems,in which the nonsmooth function is further composed with a linear operator.This problem has many applications such as sparse signal ... In this paper,we consider a class of three-composite nonconvex optimization problems,in which the nonsmooth function is further composed with a linear operator.This problem has many applications such as sparse signal recovery,image processing and machine learning.Based on the conjugate duality theory,we present an accelerated preconditioned primal-dual gradient algorithm for this problem.Compared with the existing algorithms,our algorithm only needs to calculate the proximal mapping of the conjugate function h*which is always convex and lower semicontinuous and it does not need to calculate the proximal mapping of nonconvex functions.This may significantly reduce the computation load.We prove that the sequence generated by the proposed algorithm globally converges to a critical point when the function satisfies the Kurdyka-Lojasiewicz property.We also obtain the convergence rate of the proposed algorithm.Finally,numerical results on sparse signal recovery and image processing illustrate the efficiency and competitiveness of the proposed algorithm. 展开更多
关键词 Nonconvex Composite Optimization Primal-dual Algorithm Convergence Sparse Signal Recovery Image Processing
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Enhanced Sand Cat with Selective Opposition(ESCSO)Algorithm for Optimization and Engineering Problems 认领 引用
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作者 Aisha Tanveer Noraini Ibrahim +2 位作者 Muhammad Zubair Rehman Abdullah Khan Nazri MohdNawi 《Computers, Materials & Continua》 SCIE EI 2026年第10期2104-2125,共22页
Metaheuristic optimization algorithms have gained wide adoption in engineering and scientific domains.However,many swarm-based methods struggle to balance exploration and exploitation,often converging prematurely on s... Metaheuristic optimization algorithms have gained wide adoption in engineering and scientific domains.However,many swarm-based methods struggle to balance exploration and exploitation,often converging prematurely on suboptimal solutions.The Sand Cat Swarm Optimization(SCSO)algorithm is one such method,with limited exploration ability constraining its performance on complex problem landscapes.This paper introduced the Enhanced Sand Cat with Selective Opposition(ESCSO)algorithm which combines opposition-based learning with a velocity mechanism to overcome this limitation.In ESCSO,under-performing candidates referred to as Sigma-variant cats are identified using Spearman correlation and replaced with their opposite solutions to inject diversity into the search process.Stronger candidates termed Sigma cats,act as elite guides pulling the search toward better regions.A PSO-inspired velocity update governs both roles,keeping exploration and exploitation in balance rather than letting one dominate.Tested across 30 benchmark functions plus two real engineering problems,reflectarray antenna design and microgrid energy management,ESCSO achieves competitive convergence,solution quality,and robustness when compared to recent state-of-the-art methods. 展开更多
关键词 Opposition-based learning metaheuristics search algorithm sand cat optimization swarm intelligence microgrid energy management Reflectarray antenna
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Research on the Problems and Optimization of Data Asset Accounting Confirmation and Measurement 认领 引用
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作者 Xuan Meng 《Proceedings of Business and Economic Studies》 2026年第6期76-81,共6页
The inclusion of data assets in financial statements can quantitatively reflect the value of data factors on corporate financial reports,consolidate enterprises’asset base,push enterprises to pay attention to the dev... The inclusion of data assets in financial statements can quantitatively reflect the value of data factors on corporate financial reports,consolidate enterprises’asset base,push enterprises to pay attention to the development,management,and utilization of data resources,and provide more comprehensive accounting information for stakeholders such as investors and regulatory authorities.Nevertheless,differing from traditional tangible assets and intangible assets,data assets bear distinctive particularities.They are intangible with complicated ownership relations;their values fluctuate under the influence of technological iteration,market demand,data quality,and multiple other factors,and their costs can hardly match values precisely.As a result,the conventional accounting confirmation and measurement system cannot fully adapt to the accounting treatment requirements of data assets.Therefore,systematically studying existing problems in the accounting confirmation and measurement of data assets and putting forward scientific and reasonable improvement measures carry important theoretical and practical significance. 展开更多
关键词 Data assets Accounting confirmation Accounting measurement Optimization path
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Quantum computing-enhanced topology optimization with stress constraints for truss structures 认领 引用 被引量：2
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作者 Yan Wang Dixiong Yang +1 位作者 Zhenzeng Lei Guohai Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期41-57,共17页
Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly e... Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly enhance solution efficiency in topology optimization and effectively avoid the entrapment in local optima.This paper proposes a hybrid classical-quantum computing framework to solve the stress-constrained topology optimization problem for truss structures.Initially,structural analyses are performed on a classical computer to determine the stresses of truss members.Then,the optimization problem is formulated through incremental updates of member cross-sectional areas to make it compatible with a quantum annealer.The update strategy consists of a directional-control function and a magnitude-control function.By embedding stress constraints directly into the directional-control function,the original optimization problem is reformulated as a quadratic unconstrained binary optimization model suitable for quantum annealing.To realize a balance between solution accuracy and iteration efficiency,a dynamic strategy for adjusting the magnitude of area increments is proposed.Thus,the quantum annealer can effectively achieve the optimal solutions.When only the access time of the quantum processing unit is considered,the results from 2D and 3D examples of truss topology optimization validate the effectiveness of the proposed framework,and demonstrate the great potential of quantum computing in structural optimization. 展开更多
关键词 Topology optimization Truss structures Quantum computing Quantum annealing algorithm Quadratic unconstrained binary optimization problem
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An Improved Chaotic Quantum Multi-Objective Harris Hawks Optimization Algorithm for Emergency Centers Site Selection Decision Problem 认领 引用 被引量：2
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作者 Yuting Zhu Wenyu Zhang +3 位作者 Hainan Wang Junjie Hou Haining Wang Meng Wang 《Computers, Materials & Continua》 SCIE EI 2025年第2期2177-2198,共22页
Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urge... Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems. 展开更多
关键词 Site selection triangular fuzzy theory chaotic quantum Harris Hawks optimization multi-objective optimization
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A Novel Variable-Fidelity Kriging Surrogate Model Based on Global Optimization for Black-Box Problems 认领 引用 被引量：2
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作者 Yi Guan Pengpeng Zhi Zhonglai Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第9期3343-3368,共26页
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
关键词 Global optimization kriging variable-fidelity model slime mould algorithm expected improvement
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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Solution set of circumlunar abort trajectory and its direct application to optimization design 认领 引用
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作者 Tianshan DONG Zhen HUANG +2 位作者 Wenyan ZHOU Xiangyu ZHANG Lin LU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期333-348,共16页
Circumlunar abort trajectories constitute a vital contingency return strategy during the translunar phase of crewed lunar missions.This paper proposes a methodology for constructing the solution set of the circumlunar... Circumlunar abort trajectories constitute a vital contingency return strategy during the translunar phase of crewed lunar missions.This paper proposes a methodology for constructing the solution set of the circumlunar abort trajectory and leverages its advantageous properties to address the optimization design problem of abort trajectories.Initially,a solution set of all feasible abort trajectories,originating from an abort point on the nominal trajectory and complying with fundamental reentry constraints,is formulated through the introduction of two novel design parameters.Subsequently,the geometric characteristics of the solution set,as well as the distributional properties of key iterative constraint responses,including flight time and velocity increment,are analyzed.Finally,the characteristics exhibited in the solution set are employed to directly identify the design parameters of the abort trajectories with minimum flight time and velocity increment,thereby providing solutions to two distinct types of optimization problems.The simulation results for a variety of nominal trajectories,encompassing the reconstruction and redesign of the Apollo13 abort trajectory,validate the proposed method,demonstrating its ability to directly generate optimal abort trajectories.The method proposed in this paper investigates feasible abort trajectories from a global perspective,providing both a framework and convenience for mission planning and iterative optimization in abort trajectory design. 展开更多
关键词 Circumlunar abort trajectory Design parameters Geometric characteristics Optimization problems Solution set
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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems 认领 引用
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作者 Xiaoling Wang Qi Kang +3 位作者 MengChu Zhou Qi Deng Zheng Fan Haoyue Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第6期1176-1193,共18页
To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utiliz... To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utilizing common knowledge.Knowledge transfer is the key to the effectiveness of EMT.Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance.There is plentiful knowledge in assistant tasks,and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks.EMT is thus confronted with a challenge to find appropriate knowledge.This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks.During the evolution process,better-performing candidates are supposed to have advantages in exploitation.Therefore,assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge.Specifically,the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population.Considering that target and assistant sub-populations have different characteristics,we use domain adaptation to reduce their distribution discrepancies.In this way,the trained classifier can classify assistant individuals more accurately,and truly useful knowledge can be selected for target tasks.The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems. 展开更多
关键词 Artificial intelligence evolutionary multitasking intelligent optimization inter-task learning knowledge classification knowledge transfer machine learning multiobjective optimization
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