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

DOI10.1007/S40314-020-1131-YzbMath1463.93187OpenAlexW3012331421MaRDI QIDQ2307851

Douglas Makoto Mizushima, Marcos Daniel de Freitas Awruch, Artur Dieguez Backes, Herbert Martins Gomes, Ewerton Grotti

Publication date: 25 March 2020

Published in: Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10183/223478

zbMATH Keywords

dynamics of multibody systemscomputational method stochastic programmingmulti-objective and goal programming

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59) Automated systems (robots, etc.) in control theory (93C85)

Uses Software

• PAES

Cites Work

• A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design
• A novel multiobjective quantum-behaved particle swarm optimization based on the ring model
• Design of a non-linear hybrid car suspension system using neural networks
• Evolutionary Algorithms for Solving Multi-Objective Problems

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