Multi-Source Selective Transfer Framework in Multi-Objective Optimization Problems

For complex system design (e.g., satellite layout optimization design) in practical engineering, when launching a new optimization instance with another parameter configuration from the intuition of designers, it is always executed from scratch which wastes much time to repeat the similar search process. Inspired by transfer learning which can reuse past experiences to solve relevant tasks, many researchers pay more attention to explore how to learn from past optimization instances to accelerate the target one. In real-world applications, there have been numerous similar source instances stored in the database. The primary question is how to measure the transferability from numerous sources to avoid the notorious negative transferring. To obtain the relatedness between source and target instance, we develop an optimization instance representation method named Centroid Distribution, which is by the aid of the probabilistic model learned by elite candidate solutions in Estimation of Distribution Algorithm (EDA) during the evolutionary process. Wasserstein Distance is employed to evaluate the similarity between the centroid distributions of different optimization instances, based on which, we present a novel framework called Multi-Source Selective Transfer Optimization with three strategies to select sources reasonably. To choose the suitable strategy, four selection suggestions are summarized according to the similarity between the source and target centroid distribution. The framework is beneficial to choose the most suitable sources, which could improve the search efficiency in solving multi-objective optimization problems. To evaluate the effectiveness of the proposed framework and selection suggestions, we conduct two experiments: (1) comprehensive empirical studies on complex multi-objective optimization problem benchmarks; (2) a real-world satellite layout optimization design problem. Suggestions for strategy selection coincide with the experiment results, based on which, we propose a mixed strategy to deal with the negative transfer in the experiments successfully. Results demonstrate that our proposed framework achieves competitive performance on most of the benchmark problems in convergence speed and hypervolume values and performs best on real-world applications among all the comparison algorithms.