Uncertain UAV ISR mission planning problem with multiple correlated objectives

Aiming at the selection problem for course of action (COA) of aviation swarm, this paper proposes an optimized selection method for COA of aviation swarm based on dynamic influence nets (DINs), and discrete artificial bee colony (DABC) algorithm. Firstly, based on the basic concept of the aviation swarm combat plan, static and dynamic modeling and analysis are performed, respectively. Then, the probability propagation mechanism of DINs, which mainly includes key parameter determination and probability propagation algorithm, is established. Subsequently, based on the analysis of the evaluation index, the model is solved by using DABC algorithm with real number coding. Finally, this paper takes the offshore island attack task as an example, and carries out multiple sets of simulation cases to compare DABC algorithm with discrete glowworm swarm optimization (DGSO) algorighm and discrete particle swarm optimization (DPSO) algorithm, through all these cases, the rationality of the model, and the effectiveness and superiority of the algorithm are verified. INDEX TERMS Aviation swarm, course of action, dynamic influence nets, discrete artificial bee colony algorithm, probability propagation mechanism.

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“…In this probability interval, this paper assumes that P k−1 (b l ) obeys a uniform distribution [8].…”

Section: Modeling and Analysis Of Coa A Basic Concept Of Coamentioning

confidence: 99%

COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC

Wan

Zhong²,

Wen

2020

IEEE Access

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“…When a plan is adjusted, it is unreasonable to only consider the task quality or adjustment cost. Therefore, multi-objective optimization for the problem is extremely important [20]. An optimization objective is to maximize the quality of all tasks.…”

Section: Problem Analysis and Model A Problem Analysismentioning

confidence: 99%

“…Map the current population PoP(n g ) to a feasible solution population PoP'(n g ) by using the mapping operator above. Calculate the two fitness values of the population PoP'(ng) based on (14) and (20). These are used as the fitness values of the population PoP(ng).…”

Section: Flow Of the Improved Multi-objective Evolutionary Algorithmmentioning

confidence: 99%

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Dynamic Resource Scheduling for C2 Organizations Based on Multi-Objective Optimization

et al. 2019

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The dynamic resource scheduling problem is a field of intense research in command and control organization mission planning. This paper analyzes the emergencies in the battlefield first and divides them into three categories: the changing of task attributes, reduction of available platforms, and change in the number of tasks. To deal with these emergencies, in this paper, we built a series of multiobjective optimization models that maximizes the task execution quality and minimizes the cost of plan adjustment. To solve the model, we proposed an improved multi-objective evolutionary algorithm. A type of mapping operator and an improved crowding-distance sorting method are designed for the algorithm. Finally, the availability of the model and the solving algorithm were proved through a series of experiments. The Pareto frontier for the multi-objective dynamic resource scheduling problem can be found effectively, and the algorithm proposed in this paper shows better convergence compared with the AMP-NSGA-II algorithm.INDEX TERMS Command and control organization, dynamic resource scheduling, multi-objective evolutionary algorithm, multi-objective optimization.

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“…Centralized task assignment methods including the optimization methods (decision tree method [9], dynamic programming [10], etc.) and the intelligent optimization algorithms (ant colony algorithm [11,12], particle swarm optimization algorithm [13,14], genetic algorithm [15][16][17], etc.) depend on the central control station to make the execution plan for the multi-UAV system.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Task Assignment Strategy for Heterogeneous Multi-UAV System in Large-Scale Search and Rescue Scenarios

Chen

Xiao

You

et al. 2021

International Journal of Aerospace Engineering

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For the large-scale search and rescue (S&R) scenarios, the centralized and distributed multi-UAV multitask assignment algorithms for multi-UAV systems have the problems of heavy computational load and massive communication burden, which make it hard to guarantee the effectiveness and convergence speed of their task assignment results. To address this issue, this paper proposes a hierarchical task assignment strategy. Firstly, a model decoupling algorithm based on density clustering and negotiation mechanism is raised to decompose the large-scale task assignment problem into several nonintersection and complete small-scale task assignment problems, which effectively reduces the required computational amount and communication cost. Then, a cluster head selection method based on multiattribute decision is put forward to select the cluster head for each UAV team. These cluster heads will communicate with the central control station about the latest assignment information to guarantee the completion of S&R mission. At last, considering that a few targets cannot be effectively allocated due to UAVs’ limited and unbalanced resources, an auction-based task sharing scheme among UAV teams is presented to guarantee the mission coverage of the multi-UAV system. Simulation results and analyses comprehensively verify the feasibility and effectiveness of the proposed hierarchical task assignment strategy in large-scale S&R scenarios with dispersed clustering targets.

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Uncertain UAV ISR mission planning problem with multiple correlated objectives

Cited by 20 publications

References 19 publications

COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC

COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC

Dynamic Resource Scheduling for C2 Organizations Based on Multi-Objective Optimization

Hierarchical Task Assignment Strategy for Heterogeneous Multi-UAV System in Large-Scale Search and Rescue Scenarios

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