An efficient, open-bid procurement auction for small-scale electricity markets

Lange, Sebastian; Sokolowski, Peter; Yu, Xinghuo

doi:10.1016/j.apenergy.2022.118867

Cited by 5 publications

(1 citation statement)

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“…Currently, the main task-allocation methods used in Multi-AUV systems are the swarm intelligence optimization algorithm and the Self Organizing Map (SOM) algorithm and market auction algorithm, among which the swarm intelligence optimization algorithm shows better characteristics and is widely used in AUV task allocation [14,15]. Market auction algorithms accomplish task allocation by emulating the auction process of a market economy [16]. Lee et al put forward a resource-based decentralized auction algorithm that assigns tasks to the robot that can complete the task in the shortest amount of time through reciprocal communication between the auctioneer and the robots.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

Chen,

Zhang,

Wang

et al. 2023

Electronics

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For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.

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Section: Introductionmentioning

confidence: 99%