Surrogate-assisted particle swarm optimization algorithm with Pareto active learning for expensive multi-objective optimization

For multi-objective optimization problems, particle swarm optimization (PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space (the objective functions...