Dynamic Multiobjective Optimization by Knowledge Learning Based on Factorization Machine

One of the main characteristics of dynamic multiobjective optimization problems is that the optimization objectives will change over time, so tracking the changing Pareto optimal front becomes a challenge. To this end, a knowledge learning prediction strategy based on a factorization machine is proposed (FMP). In the strategy proposed in this article, it reacts to changes by learning from the historical evolutionary process. Specifically, the factorization machine is introduced as a model to extract knowledge from the evolutionary experience of known historical time steps, and the knowledge is applied to the current time step to predict the population at the next time step. The performance of the proposed FMP is evaluated by comparing with two advanced prediction algorithms on 11 benchmark problems. Experimental results show that FMP can obtain a competitive population with good convergence and distribution in a dynamic environment.