A novel multi-objective quantum particle swarm algorithm for suspension optimization

In this paper, a novel multi-objective archive-based Quantum Particle Optimizer (MOQPSO) is proposed for solving suspension optimization problems. The algorithm has been adapted from the well-known single objective QPSO by substantial modifications in the core equations and implementation of new mul...

Full description

Saved in:
Bibliographic Details
Published in:Computational & applied mathematics Vol. 39; no. 2
Main Authors: Grotti, Ewerton, Mizushima, Douglas Makoto, Backes, Artur Dieguez, de Freitas Awruch, Marcos Daniel, Gomes, Herbert Martins
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.05.2020
Springer Nature B.V
Subjects:
Algorithms
Applications of Mathematics
Applied physics
Computational mathematics
Computational Mathematics and Numerical Analysis
Computer programming
Genetic algorithms
Mathematical Applications in Computer Science
Mathematical Applications in the Physical Sciences
Mathematics
Mathematics and Statistics
Multiple objective analysis
Numerical models
Optimization
Computational method stochastic programming
70E55
90C29
Dynamics of multibody systems
90C15
Multi-objective and goal programming
93B40
ISSN:2238-3603, 1807-0302
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a novel multi-objective archive-based Quantum Particle Optimizer (MOQPSO) is proposed for solving suspension optimization problems. The algorithm has been adapted from the well-known single objective QPSO by substantial modifications in the core equations and implementation of new multi-objective mechanisms. The novel algorithm MOQPSO and the long-established NSGA-II and COGA-II (Compressed-Objective Genetic Algorithm with Convergence Detection) are compared. Two situations are considered in this paper: a simple half-car suspension model and a bus suspension model. The numerical model of the bus allows complex dynamic interactions not considered in previous studies. The suitability of the solution is evaluated based on vibration-related ISO Standards, and the efficiency of the proposed algorithm is tested by dominance comparison. For a specifically chosen Pareto front solution found by MOQPSO in the second case, the passengers and driver accelerations attenuated about 50% and 33%, respectively, regarding non-optimal suspension parameters. All solutions found by NSGA-II are dominated by those found by MOQPSO, which presented a Pareto front noticeably wider for the same number of objective function calls.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2238-3603
1807-0302
DOI:10.1007/s40314-020-1131-y