摘要 : The optimization of multi-objective problems is an area of important research. The importance attained by this type of problems has allowed the development of multiple algorithms. To determine which multi-objective algorithm has t... 展开 The optimization of multi-objective problems is an area of important research. The importance attained by this type of problems has allowed the development of multiple algorithms. To determine which multi-objective algorithm has the best performance with respect to the problem of goods flow in the inventory, in this article an experimental comparison between two of the main multi-objective evolutionary algorithms is conducted: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The inventory model is optimized by taking into account two objectives: minimal cost of lost opportunities to make sales and minimal cost of used space in the inventory. The results obtained by both algorithms are compared and analysed based on hypervolume indicator that measures the volume of the dominated space. 收起