Prediction-based evolutionary multi-objective optimization algorithm is one of the most popular optimization algorithms for solving dynamic multi-objective optimization problem. It uses time-series models to predict the future Pareto set based on the past solutions. However, the dimension of the decision variables may be too high to predict. Moreover, a relatively small variance in decision variables may lead to a large difference in the objective space. The optimized Pareto front (PF) may be far from the desired output. To solve these problems, this paper proposes a new co-operative prediction method, which predicts not only the Pareto solution (PS), but also a hyper-plane as an approximation of the prediction of the PF in the objective space. The hyper-plane is used to guide the search process and accelerate the convergence. We compare the proposed algorithm with three existing dynamic optimization algorithms. Experimental results show the effectiveness of the proposed algorithm.