To further improve the performance of adaptive gaining-sharing knowledge-based a*algorithm (AGSK), a novel adaptive gaining sharing knowledge-based a*algorithm with historical probability expansion (HPE-AGSK) is proposed ...
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To further improve the performance of adaptive gaining-sharing knowledge-based a*algorithm (AGSK), a novel adaptive gaining sharing knowledge-based a*algorithm with historical probability expansion (HPE-AGSK) is proposed by modifying the search strategies. Based on AGSK, three improvement strategies are proposed. First, expansion sharing strategy is proposed and added in junior gaining-sharing phase to boost local search ability. Second, historical probability expansion strategy is proposed and added in senior gaining-sharing phase to strengthen global search ability. Last, reverse gaining strategy is proposed and utilized to expand population distribution at the beginning of iterations. The performance of HPE-AGSK is initially evaluated using IEEE CEC 2021 test suite, compared with fifteen state-of-the-art a*algorithms (AGSK, APGSK, APGSK-IMODE, GLAGSK, EDA2, AAVS-EDA, EBOwithCMAR, LSHADE-SPACMA, HSES, IMODE, MadDE, CJADE, and iLSHADE-RSP). The results demonstrate that HPE-AGSK outperforms both state-of-the-art GSK-based variants and past winners of IEEE CEC competitions. Subsequently, GSK-based variants and other exceptional a*algorithms in CEC 2021 are selected to further evaluate the performance of HPE-AGSK using IEEE CEC 2018 test suite. The statistical results show that HPE-AGSK has superior exploration ability than the comparison a*algorithms, and has strong competition with APGSK (state-of-the-art AGSK variant) and IMODE (CEC 2020 Winner) in exploitation ability. Finally, HPE-AGSK is utilized to solve the beyond visual range escape maneuver decision making problem. Its success rate is 100%, and mean maneuver time is 9.10 s, these results show that HPE-AGSK has good BVR escape maneuver decision-making performance. In conclusion, HPE-AGSK is a highly promising AGSK variant that significantly enhances the performance, and is an outstanding development of AGSK. The code of HPE-AGSK can be downloaded from https://***/xieleilei0305/***. (The link will b
To strengthen image security in smart agriculture, this paper presents a Two-Dimensional Super-Attractor Logistic Coupled Chaotic Model (2D-SALC). The model underwent rigorous testing against recent chaotic models, ac...
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To strengthen image security in smart agriculture, this paper presents a Two-Dimensional Super-Attractor Logistic Coupled Chaotic Model (2D-SALC). The model underwent rigorous testing against recent chaotic models, achieving a maximum Lyapunov Exponent (LE) of 13.19, a 0-1 test value of 0.9978, and a sample entropy of 2.1956, all of which passed the NIST test. Comparative analysis revealed that the 2D-SALC surpassed prior models, particularly in generating S-boxes with enhanced nonlinearity. Additionally, the integration of annealing a*algorithms and affine transformations further improved S-box performance, achieving a BIC-NL of 111.60, a BIC-SAC of 0.5007, a SAC of 0.5024, and a DP of 6, surpassing recent benchmarks. The model was also applied to XOR-based diffusion scrambling for image encryption, demonstrating greater key sensitivity, expanded key space, and higher information entropy. These results highlight the model's robustness and offer innovative solutions for enhancing image security in smart agriculture.
In assembly processes, generating paths and avoiding collisions are crucial for efficiency and safety. This paper presents a novel approach that integrates the A* pathfinding (PF) a*algorithm into FreeCAD, an open-sourc...
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In assembly processes, generating paths and avoiding collisions are crucial for efficiency and safety. This paper presents a novel approach that integrates the A* pathfinding (PF) a*algorithm into FreeCAD, an open-source Computer-Aided Design (CAD) platform. The main contribution of this work is enabling PF and collision detection directly within the CAD environment during the design phase, helping detect potential collisions early and improving the design process. The A* a*algorithm has been adapted to handle both static and dynamic obstacles inside FreeCAD. This integration allows for better planning of paths in complex assembly environments. The integration process, a*algorithm modifications and system functionality are described in detail. A case study simulating an assembly line demonstrates the a*algorithm's effectiveness in generating collision-free trajectories while adapting to dynamic changes in the environment. This work paves the way for further advancements in AIdriven CAD systems for industrial applications, enabling more intelligent and adaptive assembly processes during the design phase.
Path cover is one of the well-known NP-hard problems that has received much attention. In this paper, we study a variant of path cover, denoted by MPCv4+, to cover as many vertices in a given graph G=(V,E) as possible...
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Path cover is one of the well-known NP-hard problems that has received much attention. In this paper, we study a variant of path cover, denoted by MPCv4+, to cover as many vertices in a given graph G=(V,E) as possible by a collection of vertex-disjoint paths each of order four or above. The problem admits an existing O(vertical bar V vertical bar(8))-time 2-approximation a*algorithm by applying several time-consuming local improvement operations (Gong et al.: Proceedings of MFCS 2022, LIPIcs 241, pp 53:1-53:14, 2022). In contrast, our new a*algorithm uses a completely different method and it is an improved O(min{|E|(2)|V|(2),|V|(5)})-time 1.874-approximation a*algorithm, which answers the open question in Gong et al. (2022) in the affirmative. An important observation leading to the improvement is that the number of vertices in a maximum matching M of G is relatively large compared to that in an optimal solution of MPCv4+. Our new a*algorithm forms a feasible solution of MPCv4+ from a maximum matching M by computing a maximum-weight path-cycle cover in an auxiliary graph to connect as many edges in M as possible.
Global path planning remains a critical challenge in mobile robots, with ant colony optimization (ACO) being widely adopted for its swarm intelligence characteristics. To address the inherent limitations of ACO, this ...
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Global path planning remains a critical challenge in mobile robots, with ant colony optimization (ACO) being widely adopted for its swarm intelligence characteristics. To address the inherent limitations of ACO, this study proposes an intelligently enhanced ACO (IEACO) incorporating six innovative strategies. First, the early search efficiency is improved by implementing a non-uniform initial pheromone distribution. Second, the epsilon-greedy strategy is employed to adjust the state transition probability, thereby balancing exploration and exploitation. Third, adaptive dynamic adjustment of the exponents alpha and beta is realized, dynamically balancing the pheromone and heuristic function. Fourth, a multi-objective heuristic function considering both target distance and turning angle is constructed to enhance the quality of node selection. Fifth, a dynamic global pheromone update strategy is designed to prevent the a*algorithm from prematurely converging to local optima. Finally, by introducing multi-objective performance indicators, the path planning problem is transformed into a multi-objective optimization problem, enabling more comprehensive path optimization. Systematic simulations and experimentation were performed to validate the effectiveness of IEACO. The simulation results confirm the efficacy of each improvement in IEACO and demonstrate its performance advantages over other a*algorithms. The experimental results further highlight the practical value of IEACO in solving global path planning problems for mobile robots.
Correctly fixing the integer ambiguity of GNSS is the key to realizing the application of GNSS high-precision positioning. When solving the float solution of ambiguity based on the double-difference model epoch by epo...
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Correctly fixing the integer ambiguity of GNSS is the key to realizing the application of GNSS high-precision positioning. When solving the float solution of ambiguity based on the double-difference model epoch by epoch, the common method for resolving the integer ambiguity needs to solve the coordinate parameter information, due to the influence of limited GNSS phase data observations. This type of method will lead to an increase in the ill-posedness of the double-difference solution equation, so that the fixed success rate of the integer ambiguity is not high. Therefore, a new integer ambiguity resolution method based on eliminating coordinate parameters and ant colony a*algorithm is proposed in this paper. The method eliminates the coordinate parameters in the observation equation using QR decomposition transformation, and only estimates the ambiguity parameters using the Kalman filter. On the basis that the Kalman filter will obtain the float solution of ambiguity, the decorrelation processing is carried out based on continuous Cholesky decomposition, and the optimal solution of integer ambiguity is searched using the ant colony a*algorithm. Two sets of static and dynamic GPS experimental data are used to verify the method and compared with conventional least squares and LAMBDA methods. The results show that the new method has good decorrelation effect, which can correctly and effectively realize the integer ambiguity resolution.
A hybrid artificial bee colony a*algorithm (AC-ABC) with high robustness is proposed to solve the multiple traveling salesman problem (MTSP) with multiple depots. It initially conducts small-scale local searches to gene...
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A hybrid artificial bee colony a*algorithm (AC-ABC) with high robustness is proposed to solve the multiple traveling salesman problem (MTSP) with multiple depots. It initially conducts small-scale local searches to generate a high-quality population. Subsequently, a probabilistic model is established to balance global and local searches in the process of updating this population and exploring the optimal solution for the MTSP based on pheromone concentration and city visibility. In the process of population representation and updating, we introduce a novel tensor representation, which not only offers more opportunities for crossover between populations, but also adaptively provides more route choices to meet the personalized needs of salesmen. Besides artificial bee colony (ABC), AC-ABC takes at least 23% less execution time than other a*algorithms to solve the MTSP on multiple TSPLIB instances, especially takes about 32%-93% less execution time than the ant colony-partheno genetic a*algorithm (AC-PGA). The travel cost of the optimal route obtained by AC-ABC is significantly better than that of ABC, partheno genetic a*algorithm (PGA), improved PGA (IPGA), and two-part wolf pack search (TWPS). AC-ABC always obtains less travel cost than AC-PGA when the number of cities n <= 150. AC-ABC only obtains about 0.5%-7.4% more travel cost than AC-PGA when the number of cities n> 150.
In recent years, the Adaptive Antoulas-Anderson (AAA) a*algorithm has been applied extensively for generating Reduced Order Models (ROMs) of large scale Linear Time-Invariant systems starting from measurements of their ...
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In recent years, the Adaptive Antoulas-Anderson (AAA) a*algorithm has been applied extensively for generating Reduced Order Models (ROMs) of large scale Linear Time-Invariant systems starting from measurements of their transfer functions. When used for Model Order Reduction (MOR) of an asymptotically stable system, the ROMs generated applying AAA are not guaranteed to preserve this fundamental property, thus rendering them impractical in many relevant applications. To overcome this issue, we propose a novel algebraic characterization for the stability of ROMs represented under the AAA barycentric structure. We then translate such characterization into a set of convex semidefinite constraints that can be embedded into the AAA optimization routine to explicitly maximize the model accuracy while ensuring its stability. Finally, we generalize the resulting modeling framework to allow for efficient stable MOR of Multi-Input-Multi-Output systems. An extensive set of numerical experiments provides practical evidence for the effectiveness of the proposed approach and compares its performance with those of available state-of-the-art methods.
The problem of joint observation and transmission scheduling with energy constraint is very important, but has received limited attention so far. To address the joint scheduling problem considering energy consumption ...
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The problem of joint observation and transmission scheduling with energy constraint is very important, but has received limited attention so far. To address the joint scheduling problem considering energy consumption and supplement, a new constraint satisfaction model with energy constraint for multiple agile satellites is established. Then a Local Search Enhanced Ant Colony Optimization with Multi-Knapsack Task Assignment (LSE-ACO-MKTA) a*algorithm is proposed, integrating observation, transmission, and charging into a unified planning framework. The a*algorithm employs a multi-knapsack-based task assignment strategy and local search enhanced ACO a*algorithm, significantly reducing the dimension of the original problem. The first simulation experiment validated the necessity of joint scheduling with energy constraint. After that, two comparative experiments were conducted to discuss the mechanism of local search and task assignment and then proved the efficiency of LSE-ACO-MKTA.
作者：
Zhang, Yu-tingWang, Zhen-chunMinist Educ
Intelligent Control Syst & Intelligent Equipment E Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ
Key Lab Ind Comp control Engn Qinhuangdao 066004 Hebei Peoples R China
Modeling the electromagnetic rail launch is a critical aspect that provides solid theoretical support for the design and optimization of launch systems. This paper comprehensively considers factors such as armature sh...
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Modeling the electromagnetic rail launch is a critical aspect that provides solid theoretical support for the design and optimization of launch systems. This paper comprehensively considers factors such as armature shape, rail shape, and the skin effect of current to establish a dynamic electromagnetic field model for an H-shaped armature in electromagnetic rail launch. To achieve efficient solutions, an adaptive recursive numerical analysis method is proposed. This method recursively segments the integration intervals of various variables and employs adaptive Simpson's numerical integration for regression once the integration nodes are determined. This successfully enables the rapid resolution of the dynamic electromagnetic field model, yielding a time-varying inductance gradient. Subsequently, the Runge-Kutta method is used to calculate the velocity and displacement curves of the armature. Experimental validation of the model indicates a 2.28% error in the calculated muzzle velocity of the armature and a maximum error of 4.48% during the launch process. These results strongly validate the proposed model and the adaptive recursive numerical analysis method, providing strong evidence for theoretical analysis in electromagnetic launch technology.
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