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 this, fast nondominated 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 multi-objective simulated annealing (mosa) and non-dominated sorting genetic algorithms (nsga-ii) presented as state-of the-art in evolutionary multi-objective optimization algorithms. the results indicate that multi-objective vdo (movdo) gives better performance with a significant difference interms of computational time, while nsga-ii is better in finding 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.