Transfer Learning based Dynamic Multiobjective Optimization Algorithms
This work addresses the challenge of tracking changing Pareto-optimal fronts in DMOPs for optimization researchers, offering an incremental improvement by applying transfer learning to existing methods.
The paper tackles dynamic multiobjective optimization problems (DMOPs) by proposing Tr-DMOEA, an algorithmic framework that integrates transfer learning with evolutionary algorithms to reuse past experiences and speed up the evolutionary process, showing effectiveness through experiments on twelve benchmark functions.
One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is the optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing the "experiences" to construct a prediction model via statistical machine learning approaches. However most of the existing methods ignore the non-independent and identically distributed nature of data used to construct the prediction model. In this paper, we propose an algorithmic framework, called Tr-DMOEA, which integrates transfer learning and population-based evolutionary algorithm for solving the DMOPs. This approach takes the transfer learning method as a tool to help reuse the past experience for speeding up the evolutionary process, and at the same time, any population based multiobjective algorithms can benefit from this integration without any extensive modifications. To verify this, we incorporate the proposed approach into the development of three well-known algorithms, nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle swarm optimization (MOPSO), and the regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), and then employ twelve benchmark functions to test these algorithms as well as compare with some chosen state-of-the-art designs. The experimental results confirm the effectiveness of the proposed method through exploiting machine learning technology.