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High-Dimensional Multiobjective Optimization Design for Magnetic Stealth of Underwater Vehicle Based on Improved MSOPS Algorithm | IEEE Journals & Magazine | IEEE Xplore

High-Dimensional Multiobjective Optimization Design for Magnetic Stealth of Underwater Vehicle Based on Improved MSOPS Algorithm


Abstract:

The underwater vehicle will generate multiple magnetic features, such as peak, gradient, rate of change, and so on, in multiple directions. In order to guarantee magnetic...Show More

Abstract:

The underwater vehicle will generate multiple magnetic features, such as peak, gradient, rate of change, and so on, in multiple directions. In order to guarantee magnetic stealth performance and improve survivability, the low-dimensional optimization cannot seek the true Pareto front of each objective, and this article proposes to carry out high-dimensional multiobjective optimization and control of the demagnetization system of the underwater vehicle and introduce the high-dimensional algorithms to generate the Pareto front on the hyperplane to provide the decision makers with choices. This article proposes the multiple single objective pareto sampling (IMSOPS) that combines the parallel search idea of multiple single objectives with a genetic algorithm, utilizes the ability of the genetic algorithm to retain the strong dominant individuals, adds initial boundaries, and realizes the high-dimensional multiobjective algorithm with fast convergence. Also, nondominated sorting and congestion operations are added to further improve the diversity of the algorithm. Using underwater vehicle simulation experiments and comparing with other high-dimensional algorithms, the improved algorithm is analyzed for its superiority in convergence speed, convergence, diversity, and comprehensiveness. Finally, the effectiveness of the IMSOPS algorithm implemented on the magnetic stealth technology of underwater vehicles is verified through the experiment of a real scaled-down model.
Article Sequence Number: 2503913
Date of Publication: 18 December 2023

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