In this chapter, generalization ability of swarm intelligence algorithms solving problems with different number of dimensions is analyzed and discussed. Three algorithms, brain storm optimization in objective space (BSO-OS), fireworks algorithm (FWA), and particle swarm optimization (PSO) algorithm, are selected as illustrations to explain the definition of algorithm's generalization ability. The performance of BSO-OS, FWA, and PSO algorithm on solving problems with different number of dimensions is analyzed. Based on the experimental results, the algorithm's generalization ability was measured by the results ratio of algorithms with the same settings on problems with different number of dimensions. This generalization ability measurement could be extended to problems with different components. Without analysis on the landscape of problems, this measurement could give a practical illustration of the generalization ability of algorithms for solving problems with different number of dimensions or different components. Based on the analysis on the generalization of algorithms and the hardness of problems, we could have a better understanding of the relationship between problems and algorithms, and therefore design more effective algorithms to solve different problems.