Dynamic multi-objective evolutionary algorithm based on knowledge transfer

Dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective changes with changes in the environment. To solve DMOPs, a transfer learning (TL) approach is used, which can continuously adapt to environmental changes and reuse valuable knowledge from the past. However, if all individuals are transferred, they may experience negative transfers. Therefore, this paper proposes a novel knowledge transfer method for the dynamic multi-objective evolutionary algorithm (T-DMOEA) to solve DMOPs, which consists of a multi-time prediction model (MTPM) and a manifold TL algorithm. First, according to the movement trend of historical knee points, the MTPM model uses a weighted method to effectively track knee points after environmental changes. Then, the knowledge of the suboptimal solution is reused in the non -knee point set using the manifold TL technique, which yields more high-quality individuals and speeds up the convergence. In the dynamic evolutionary process, the knee points and high-quality solutions are combined to guide the generation of the initial population in the next environment, ensuring the diversity of the population while reducing the computational cost. The experimental results show that the proposed T-DMOEA algorithm can converge rapidly in solving DMOPs while obtaining better-quality solutions.

Automated summary

Dynamic multi-objective optimization problems are characterized by objective changes with changes in the environment. To solve this problem, a transfer learning approach is used to continuously adapt to environmental changes and reuse valuable knowledge from the past. This paper proposes a novel knowledge transfer method for the dynamic multi-objective evolutionary algorithm (T-DMOEA), which effectively tracks knee points after environmental changes and reuses suboptimal solutions using manifold transfer learning technique, resulting in high-quality solutions and faster convergence.