Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulner...Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulnerabilities and threats to the security and resilience of critical infrastructures.However,achieving efficient path optimization in complex large-scale three-dimensional(3D)scenes remains a significant challenge for vulnerability assessment.This paper introduces a novel A^(*)-algorithmic framework for 3D security modeling and vulnerability assessment.Within this framework,the 3D facility models were first developed in 3ds Max and then incorporated into Unity for A^(*)heuristic pathfinding.The A^(*)-heuristic pathfinding algorithm was implemented with a geometric probability model to refine the detection and distance fields and achieve a rational approximation of the cost to reach the goal.An admissible heuristic is ensured by incorporating the minimum probability of detection(P_(D)^(min))and diagonal distance to estimate the heuristic function.The 3D A^(*)heuristic search was demonstrated using a hypothetical laboratory facility,where a comparison was also carried out between the A^(*)and Dijkstra algorithms for optimal path identification.Comparative results indicate that the proposed A^(*)-heuristic algorithm effectively identifies the most vulnerable adversarial pathfinding with high efficiency.Finally,the paper discusses hidden phenomena and open issues in efficient 3D pathfinding for security applications.展开更多
Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food s...Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food source.This algorithm has the characteristics of a simple structure and high performance.However,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation capabilities.This paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization problems.Firstly,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the algorithm.Secondly,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst solutions.Thirdly,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence speed.These three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the algorithm.In performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic algorithms.The numerical experimental results demonstrated the superiority of APFA.展开更多
针对已有的算法在基于到达时间差(time difference of arrival,TDOA)测量方案中存在的搜索能力不均衡,导致三维定位区域局部存在定位精度低甚至求解失败的问题,提出了一种基于改进探路者优化算法(pathfinder algorithm,PFA)的TDOA定位算...针对已有的算法在基于到达时间差(time difference of arrival,TDOA)测量方案中存在的搜索能力不均衡,导致三维定位区域局部存在定位精度低甚至求解失败的问题,提出了一种基于改进探路者优化算法(pathfinder algorithm,PFA)的TDOA定位算法,通过将自适应Levy飞行和改进后的PFA算法进行融合,增强了个体对定位区域复杂环境的适应性,解决算法早熟、易陷入局部最优等问题,提升了算法综合性能.通过仿真和实验,结果表明:与Taylor算法、LM算法相比,本文提出的算法(Levy-pathfinder algorithm,LPFA)可以提高定位精度;与PSO算法、PFA算法相比,LPFA算法可以在提高运算速度的同时得到更准确的定位结果.展开更多
基金supported by the fundings from 2024 Young Talents Program for Science and Technology Thinking Tanks(No.XMSB20240711041)2024 Student Research Program on Dynamic Simulation and Force-on-Force Exercise of Nuclear Security in 3D Interactive Environment Using Reinforcement Learning,Natural Science Foundation of Top Talent of SZTU(No.GDRC202407)+2 种基金Shenzhen Science and Technology Program(No.KCXFZ20240903092603005)Shenzhen Science and Technology Program(No.JCYJ20241202124703004)Shenzhen Science and Technology Program(No.KJZD20230923114117032)。
文摘Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulnerabilities and threats to the security and resilience of critical infrastructures.However,achieving efficient path optimization in complex large-scale three-dimensional(3D)scenes remains a significant challenge for vulnerability assessment.This paper introduces a novel A^(*)-algorithmic framework for 3D security modeling and vulnerability assessment.Within this framework,the 3D facility models were first developed in 3ds Max and then incorporated into Unity for A^(*)heuristic pathfinding.The A^(*)-heuristic pathfinding algorithm was implemented with a geometric probability model to refine the detection and distance fields and achieve a rational approximation of the cost to reach the goal.An admissible heuristic is ensured by incorporating the minimum probability of detection(P_(D)^(min))and diagonal distance to estimate the heuristic function.The 3D A^(*)heuristic search was demonstrated using a hypothetical laboratory facility,where a comparison was also carried out between the A^(*)and Dijkstra algorithms for optimal path identification.Comparative results indicate that the proposed A^(*)-heuristic algorithm effectively identifies the most vulnerable adversarial pathfinding with high efficiency.Finally,the paper discusses hidden phenomena and open issues in efficient 3D pathfinding for security applications.
基金supported by the National Natural Science Foundation of China under Grant No.61802328,61972333,and 61771415.
文摘Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food source.This algorithm has the characteristics of a simple structure and high performance.However,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation capabilities.This paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization problems.Firstly,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the algorithm.Secondly,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst solutions.Thirdly,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence speed.These three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the algorithm.In performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic algorithms.The numerical experimental results demonstrated the superiority of APFA.
