This paper studies a class of distributed optimization problems, which aims to minimize the global cost function consisting of the sum of local cost functions through local information exchanges. For this class of problems, by introducing a time-based generator(TBG),the paper proposes two distributed proportional-integral (PI) optimization algorithms based on predefined-time convergence. Compared to existing distributed optimization algorithms based on finite/fixed time convergence, the convergence time of the proposed algorithms does not depend on initial values and parameters of the system and it can be arbitrarily predefined. Furthermore, the proposed algorithms can converge within a predefined time based on the Lyapunov theory under the conditions that the global cost function is restricted strongly convex with respect to the global optimal point along with local cost functions being convex, differentiable, and having local Lipschitz gradient. Finally, the effectiveness of these two algorithms is verified by numerical simulation.