Many real-world problems are dynamic multi-objective optimization problem.This kind of problem has multiple objectives

and these objectives change constantly due to the influence of environment.In this paper

a prediction strategy based on guide-individual (GIPS) is proposed.When environment changes

by recording the different center positions between populations in the initial environment and the ones evolving independently after a short time

GIPS predicts the direction of the optimal solutions.Moreover

from uniformly distributed individuals detected in the prediction direction

GIPS selects a bunch of non-dominated individuals as the guide-individuals for the current environment.In addition

the proposed strategy randomly generates a number of associated guide-individuals within a small area in order to avoid population to be trapped into local optimum.Compared with two state-of-the-art prediction-based dynamic multi-objective optimization algorithms

GIPS show faster response to the environmental changes.