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
In order to investigate the penetration performance of Linear-Shaped Charge(LSC),Embowed LinearShaped Charge(ELSC),and Embowed Linear Explosively Formed Projectile(ELEFP)on T-shaped stiffened plates,a series of near-f...In order to investigate the penetration performance of Linear-Shaped Charge(LSC),Embowed LinearShaped Charge(ELSC),and Embowed Linear Explosively Formed Projectile(ELEFP)on T-shaped stiffened plates,a series of near-field air-burst experiments are conducted.The damage modes and characteristics of the target plates are compared and analyzed.Each flat plate section is completely punctured,resulting in a penetration hole.The damage modes induced by the three charge types on the stiffened plate structure are consistent,characterized by shear failure in the central region of the flat plate due to penetration by the penetrator,localized plastic deformation of the flat plate,and local penetration failure resulting from partial perforation of the central stiffener.The penetration lengths caused by ELSC and ELEFP are 45.1%and 46.1% larger than that of LSC,while the half-width of the penetration hole generated by ELEFP is 54.2% and 24.7% smaller than that of ELSC and LSC,respectively.The penetration height caused by ELEFP are 17.5%and 62.1% larger than that of ELSC and LSC,respectively.The stiffener effectively segments the damage area,enhancing the local structural strength and limiting the extent of plastic deformation in the flat plate section.The comparative results show that the ELSC proves to be more effective for efficient large-scale damage,and ELEFP is more suitable for achieving efficient localized damage.展开更多
As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This pap...As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This paper presents an Iterative Safe Dispatch Corridor(iSDC)framework,addressing the suboptimality of the traditional SDC method caused by static corridor construction and redundant obstacle exploration.First,a Kinodynamic-Informed-Bidirectional Rapidly-exploring Random Tree Star(KIBRRT^(*))algorithm is proposed for the front-end coarse planning.By integrating bidirectional tree expansion,goal-biased elliptical sampling,and artificial potential field guidance,it reduces unnecessary exploration near concave obstacles and generates kinematically admissible paths.Secondly,the traditional SDC is implemented in an iterative manner,and the obtained trajectory in the current iteration is fed into the next iteration for corridor generation,thus progressively improving the quality of withincorridor constraints.For tractors,a reverse-motion penalty function is incorporated into the back-end optimizer to prioritize forward driving,aligning with mechanical constraints and human operational preferences.Numerical validations on the data of Gerald R.Ford-class carrier demonstrate that the KIBRRT^(*)reduces average computational time by 75%and expansion nodes by 25%compared to conventional RRT^(*)algorithms.Meanwhile,the iSDC framework yields more time-efficient trajectories for both carrier aircraft and tractors,with the dispatch time reduced by 31.3%and tractor reverse motion proportion decreased by 23.4%relative to traditional SDC.The presented framework offers a scalable solution for autonomous dispatch in confined and safety-critical environment,and an illustrative animation is available at bilibili.com/video/BV1tZ7Zz6Eyz.Moreover,the framework can be easily extended to three-dimension scenarios,and thus applicable for trajectory planning of aerial and underwater vehicles.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supported by the National Natural Science Foundation of China(Grant Nos.52271307,52061135107,52192692,11802025)the Liao Ning Excellent Youth Fund Program(Grant No.2023JH3/10200012)+1 种基金the Liao Ning Revitalization Tal-ents Program(Grant No.XLYC1908027)the Fundamental Research Funds for the Central Universities(Grant Nos.DUT20RC(3)025,DUT20TD108,DUT20LAB308)。
文摘In order to investigate the penetration performance of Linear-Shaped Charge(LSC),Embowed LinearShaped Charge(ELSC),and Embowed Linear Explosively Formed Projectile(ELEFP)on T-shaped stiffened plates,a series of near-field air-burst experiments are conducted.The damage modes and characteristics of the target plates are compared and analyzed.Each flat plate section is completely punctured,resulting in a penetration hole.The damage modes induced by the three charge types on the stiffened plate structure are consistent,characterized by shear failure in the central region of the flat plate due to penetration by the penetrator,localized plastic deformation of the flat plate,and local penetration failure resulting from partial perforation of the central stiffener.The penetration lengths caused by ELSC and ELEFP are 45.1%and 46.1% larger than that of LSC,while the half-width of the penetration hole generated by ELEFP is 54.2% and 24.7% smaller than that of ELSC and LSC,respectively.The penetration height caused by ELEFP are 17.5%and 62.1% larger than that of ELSC and LSC,respectively.The stiffener effectively segments the damage area,enhancing the local structural strength and limiting the extent of plastic deformation in the flat plate section.The comparative results show that the ELSC proves to be more effective for efficient large-scale damage,and ELEFP is more suitable for achieving efficient localized damage.
基金support of the National Key Research and Development Plan(Grant No.2021YFB3302501)the financial support of the National Science Foundation of China(Grant No.12161076)the financial support of the Fundamental Research Funds for the Central Universities(Grant No.DUT24LAB129).
文摘As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This paper presents an Iterative Safe Dispatch Corridor(iSDC)framework,addressing the suboptimality of the traditional SDC method caused by static corridor construction and redundant obstacle exploration.First,a Kinodynamic-Informed-Bidirectional Rapidly-exploring Random Tree Star(KIBRRT^(*))algorithm is proposed for the front-end coarse planning.By integrating bidirectional tree expansion,goal-biased elliptical sampling,and artificial potential field guidance,it reduces unnecessary exploration near concave obstacles and generates kinematically admissible paths.Secondly,the traditional SDC is implemented in an iterative manner,and the obtained trajectory in the current iteration is fed into the next iteration for corridor generation,thus progressively improving the quality of withincorridor constraints.For tractors,a reverse-motion penalty function is incorporated into the back-end optimizer to prioritize forward driving,aligning with mechanical constraints and human operational preferences.Numerical validations on the data of Gerald R.Ford-class carrier demonstrate that the KIBRRT^(*)reduces average computational time by 75%and expansion nodes by 25%compared to conventional RRT^(*)algorithms.Meanwhile,the iSDC framework yields more time-efficient trajectories for both carrier aircraft and tractors,with the dispatch time reduced by 31.3%and tractor reverse motion proportion decreased by 23.4%relative to traditional SDC.The presented framework offers a scalable solution for autonomous dispatch in confined and safety-critical environment,and an illustrative animation is available at bilibili.com/video/BV1tZ7Zz6Eyz.Moreover,the framework can be easily extended to three-dimension scenarios,and thus applicable for trajectory planning of aerial and underwater vehicles.
