A Particle Swarm Optimization Algorithm Based on Experience Pool for Multi-UAV Cooperative Reconnaissance Task Allocation

Wang, Gang; Lv, Xiao; Ben, Kerong; Cui, LiangZhong

doi:10.1109/cscwd57460.2023.10152808

Cited by 4 publications

(9 citation statements)

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“…The f d i represents the entire flight distance of U i departure from the platform to completing the mission and returning to the platform. According to our previous work [27],…”

Section: ) Completeness Constraintmentioning

confidence: 99%

“…The completion time of reconnaissance task CT and average time of flight AT need to be comprehensively considered in objective function. The definition and calculation formula of CT and AT can be seen in our previous work [27]. The objective function is defined as follows:…”

Section: Objective Functionmentioning

confidence: 99%

“…[31][32][33][34], the following parameter adjustment strategy is adopted to improve the optimisation ability of EPPSO. The setting of parameters are referred to common practices and some experiments, and have been elaborated in our previous work [27]. The specific values of parameters are shown in Section 5.…”

Section: Time-varying Parameter Adjustment Strategymentioning

confidence: 99%

“…There are two algorithms contained in the proposed method: EPPSO and CNP‐PTR. The EPPSO has been proposed in our previous work [27], which improves the optimisation ability through a novel method. Compared with existing PSO‐based algorithms, the core of EPPSO algorithm is the particle position reconstruction strategy based on experience pool.…”

Section: Introductionmentioning

confidence: 99%

“…As a great extension of CSCWD2023 paper [27], this paper establishes a multi‐UAV cooperative reconnaissance task pre‐allocation and reallocation (MCRTPR) model. And the methods of task pre‐allocation and reallocation for the MCRTPR model are proposed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The methods of task pre‐allocation and reallocation for multi‐UAV cooperative reconnaissance mission

Wang,

Lv,

Cui

et al. 2023

IET Collab Intel Manufact

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Nowadays, multi unmanned aerial vehicle (multi‐UAV) systems have been widely used in battlefield. The rationality of mission plan can directly affect the effectiveness of multi‐UAV system. The existing multi‐UAV task allocation model lack a comprehensive modelling of task pre‐allocation and task reallocation issues. However, in actual task execution, task pre‐allocation and task reallocation are a holistic problem. Therefore, based on the background of multi‐UAV cooperative reconnaissance, the authors establish a multi‐UAV cooperative reconnaissance task pre‐allocation and reallocation model (MCRTPR). There are two kinds of task allocation in MCRTPR model. One is task pre‐allocation, which is a static task allocation before the mission begin. Another is task reallocation, that is a dynamic task allocation during the mission. For task pre‐allocation, a particle swarm optimisation algorithm based on experience pool (EPPSO) is proposed. And for task reallocation, the authors design a partial task reallocation algorithm based on contract network protocol (CNP‐PTR). The experimental results show that, compared with some state‐of‐the‐art algorithms, EPPSO can get the lowest fitness value under various experimental conditions, and CNP‐PTR is able to handle task reallocation problem caused by multiple kinds of dynamic events.

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“…The f d i represents the entire flight distance of U i departure from the platform to completing the mission and returning to the platform. According to our previous work [27],…”

Section: ) Completeness Constraintmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%