Solving dynamic multi-objective optimisation problem means to search adaptively for the Pareto optimal solutions when the environmental changes. It is important to find out the changing pattern for the efficiency of the dynamic search. Learning techniques are thus widely used to explore the dependence structure of the changing for population re-initialisation in the evolutionary search paradigm. The learning techniques are expected to discover some useful knowledge from history information, while the learned knowledge can help improve the search speed through good initialisation when change occurs. In this paper, we propose a new learning strategy based on the incorporation of mutual information, stable matching strategy and Newton's laws of motion, where mutual information is used to identify the relationship between previously found approximated solutions; the stable matching strategy is to associate previous found optimal solutions in a global manner and the Newton's Laws of motion is applied to re-initialise the new population. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.
