A novel multi-objective quantum particle swarm algorithm for suspension optimization - Grotti, Ewerton; Mizushima, Douglas Makoto; Backes, Artur Dieguez; de Freitas Awruch, Marcos Daniel; Gomes, Herbert Martins (Computational & applied mathematics, 2020)
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.