  Dynamic multi-objective optimization problems (DMOPs) remain a challenge to
be settled, because of conflicting objective functions change over time. In
recent years, transfer learning has been proven to be a kind of effective
approach in solving DMOPs. In this paper, a novel transfer learning based
dynamic multi-objective optimization algorithm (DMOA) is proposed called
regression transfer learning prediction based DMOA (RTLP-DMOA). The algorithm
aims to generate an excellent initial population to accelerate the evolutionary
process and improve the evolutionary performance in solving DMOPs. When an
environmental change is detected, a regression transfer learning prediction
model is constructed by reusing the historical population, which can predict
objective values. Then, with the assistance of this prediction model, some
high-quality solutions with better predicted objective values are selected as
the initial population, which can improve the performance of the evolutionary
process. We compare the proposed algorithm with three state-of-the-art
algorithms on benchmark functions. Experimental results indicate that the
proposed algorithm can significantly enhance the performance of static
multi-objective optimization algorithms and is competitive in convergence and
diversity.
