Multiobjective game method based on self-adaptive space division of design variables and its application to vehicle suspension
type: Item
instance of: scholarly article
title: Multiobjective game method based on self-adaptive space division of design variables and its application to vehicle suspension
zbMATH Open document ID: 1407.90298
DOI: 10.1155/2014/479272
author: Rui Meng
author: Lu Wang
author: Neng-Gang Xie
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Based on the similarity between the game theory and the multiobjective design, the bionic mapping and the space mapping are established between the multiobjective optimization model and game model. Then, the multiobjective optimization method based on self-adaptive space division of design variables is proposed. The design variables are divided into multiple strategy subspaces and are assigned to corresponding game players by calculating impact factors, \(K\)-means clustering, and correlation analysis. Strategy subspaces of game players are dynamically adjusted in the iteration process. In their own strategy subspaces, each game player takes their payoff function (the mapping of objective function) as monoobjective optimization. It gives the best strategy upon other players. And the best strategies of all players are combined into the group strategy in this game round. Triobjective optimization is carried out for vehicle suspension in this method and it is compared with the traditional game method. The results show that this method has better calculating automaticity and can effectively promote generalization of multiobjective game method and improve the computational efficiency and precision.
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 91A80
zbMATH DE Number: 7016529
Wikidata QID: Q59067718
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/479272
OpenAlex ID: W2093445986
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description: scientific article; zbMATH DE number 7016529