A novel Pareto-based multi-objective vibration damping optimization ‎algorithm to solve multi-objective optimization problems

This paper presents a vibration damping optimization (VDO) algorithm to solve multi-objective optimization problems for the first time. To do that, fast non-dominated sorting and crowding distance concepts were used in order to find and manage the Pareto-optimal solution. The proposed VDO is validated using several examples taken from the literature. The results were compared with the multi-objective simulated annealing (MOSA) and non-dominated sorting genetic algorithms (NSGA-II) presented as the state-of-the-art in evolutionary multi-objective optimization algorithms. The results indicate that multi-objective VDO (MOVDO) shows better performances with significant difference in terms of computational timewhile NSGA-IIis better to find Pareto solutions. In other standard metrics, MOVDO is able to generate true and well-distributed Pareto optimal solutions and compete with NSGA-II and MOSA.