Multi-weapon multi-target assignment based on hybrid genetic algorithm in uncertain environment

Yang, Zhao; Chen, Yifei; Zhen, Ziyang; Jiang, Ju

doi:10.1177/1729881420905922

Cited by 29 publications

(11 citation statements)

References 33 publications

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“…If the value of σ is 0, then the search is sluggish and "early maturation" has occurred, which requires a threshold value of . τ [14]The fitness of the solution is compared with that obtained by the current algorithm. If the algorithm fails to find a better solution in L consecutive iterations (i.e., the fitness is less than the threshold τ ), the algorithm is determined to be stuck in a local optimum.…”

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confidence: 99%

An IGA model for resource allocation in heterogeneous networks for the internet of things

Xiao

2023

Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023)

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To reasonably allocate the heterogeneous network resources of Internet of Things (IoT) to meet the various needs of users in a cost-effective manner. The study proposes an improved genetic algorithm (IGA) method for finding the optimal solution of heterogeneous network resource allocation. The method first transforms the IoT heterogeneous network resource allocation optimization problem into an objective function to find the optimal solution problem, then uses the improved genetic algorithm to find the optimal solution under the constraints, and finally analyses and simulates the performance of the model for application. The results show that the algorithm stabilises after 10 iterations in the IoT-23 dataset and 14 iterations in the IoT-Healthcare dataset, with stability values of approximately 3.16 and 3.14 respectively, and that the computation time of the algorithm is 8.8m and 3.6m in the two datasets respectively when the number of users is 100, which is much less than the other two algorithms. In the simulation experiments on the accuracy of the calculation results, the study achieved an accuracy rate of over 92% and 94% in A and B respectively. Combining the above data, it can be shown that the method can accurately achieve the efficient allocation and utilisation of resources under the constructed network environment and demand.(Abstract)

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confidence: 99%

An IGA model for resource allocation in heterogeneous networks for the internet of things

Xiao

2023

Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023)

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“…As an essential function of the target analysis Decision Support System, target threat assessment aims to analyze the potential threat of incoming targets and get a numerical value of the d enemy target's threat. Target threat assessment utilizes multiple character of the target such as movement parameter, electromagnet parameter, identity information and so on to evaluate the threat degree of the incoming targets and gives a reasonable reference rule for weapon target distribution [1]. So the research on target threat assessment should be carried out immediately to help the commander to make decision conveniently.…”

Section: Introductionmentioning

confidence: 99%

Target Threat Assessment in Air Combat with BP Neural Network for UAV

Lei¹,

Li²,

Wu³

et al. 2023

J. Phys.: Conf. Ser.

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Target threat assessment is the most important element in command control communication and computer intelligence surveillance reconnaissance (C4ISR) system which is the key function in air combat situation analysis and can select the most valuable target to the commander. A threat assessment model of multi-UAV platform cooperative engagement built based on back propagate neural network (BPNN) is present in this paper, which provides a machine learning method for the threat assessment. The BPNN target threat assessment model has six input nodes, 10 hide nodes and 1 output node, which can fit the complicated nonlinear relationship between threat value and target behavior state. Simulation experiment is executed to verify the accuracy of the proposed BPNN target threat assessment method, and the result shows that the method is effective and can be applied to the real air combat.

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“…Essentially, the WTA problem can be formulated as a nonlinear integer programming problem and is known to be non-deterministic polynomical (NP)-complete [10]. The WTA model can be divided into the static WTA (SWTA) model and the dynamic WTA (DWTA) model from the perspective of whether time is considered as a factor [11,12]. SWTA launches all defense weapons at a certain stage to find the best allocation for temporary defense tasks [13].…”

Section: Introductionmentioning

confidence: 99%

Shuffled Frog Leaping Algorithm with Non-Dominated Sorting for Dynamic Weapon-Target Assignment

Zhao,

Liu,

Jiang

et al. 2023

J. of Syst. Eng. Electron.

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The dynamic weapon target assignment (DWTA) problem is of great significance in modern air combat. However, DWTA is a highly complex constrained multi-objective combinatorial optimization problem. An improved elitist non-dominated sorting genetic algorithm-II (NSGA-II) called the non-dominated shuffled frog leaping algorithm (NSFLA) is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints. In NSFLA, the shuffled frog leaping algorithm (SFLA) is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm (GA), displaying low optimization speed and heterogeneous space search defects. Two improvements have also been raised to promote the internal optimization performance of SFLA. Firstly, the local evolution scheme, a novel crossover mechanism, ensures that each individual participates in updating instead of only the worst ones, which can expand the diversity of the population. Secondly, a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search. Finally, the scheme is verified in various air combat scenarios. The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency, especially in many aircraft and the dynamic air combat environment.

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Multi-weapon multi-target assignment based on hybrid genetic algorithm in uncertain environment

Cited by 29 publications

References 33 publications

An IGA model for resource allocation in heterogeneous networks for the internet of things

An IGA model for resource allocation in heterogeneous networks for the internet of things

Target Threat Assessment in Air Combat with BP Neural Network for UAV

Shuffled Frog Leaping Algorithm with Non-Dominated Sorting for Dynamic Weapon-Target Assignment

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