2019
DOI: 10.24846/v28i1y201911
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Optimisation of Linear Passive Suspension System Using MOPSO and Design of Predictive Tool with Artificial Neural Network

Abstract: One of the main challenges in the design of passive suspension systems is the optimum selection of suspension system parameters. In this paper, a-four-degree-of-freedom quarter car model is implemented in order to design an optimal suspension system for better ride comfort and road holding characteristics. The mathematical model was generated in MATLAB Simulink environment for simulation. The Multi-objective particle swarm optimisation algorithm is used to optimise the suspension parameters such as suspension … Show more

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Cited by 8 publications
(8 citation statements)
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“…e disadvantage is that the subjectivity is relatively strong, and the conclusion is difficult to converge in the evaluation of multiple people. It is suitable for decision-making analysis objects at the strategic level, and systems that cannot be quantified are difficult to quantify [10].…”
Section: Experts Grading Methodmentioning
confidence: 99%
“…e disadvantage is that the subjectivity is relatively strong, and the conclusion is difficult to converge in the evaluation of multiple people. It is suitable for decision-making analysis objects at the strategic level, and systems that cannot be quantified are difficult to quantify [10].…”
Section: Experts Grading Methodmentioning
confidence: 99%
“…MOPSO uses the mutation operator to maintain diversity and dispersion in the solution space and avoid stuck in a local minimum. 38…”
Section: Multi-objective Optimizationmentioning
confidence: 99%
“…rough evaluations run on a large dataset from Twitter, they have demonstrated that the proposed method outperforms competitive baseline methods effectively. However, the present work does not consider the use of other types of data in microblogs for hashtag recommendation [20].…”
Section: Literature Reviewmentioning
confidence: 99%