Transfer learning is an effective method to solve dynamic multi-objective optimization problems,but when the difference between the source domain and the target domain is large,negative transfer will occur,which greatly re-duces the efficiency of solving optimization problems.Aiming at this phenomenon,this algorithm proposes a transfer learning method based on the source domain selection strategy.The method first sorts the historical environmental da-ta according to the difference between the optimal solution set of the historical environment and the target domain of the new environment,and selects a historical environmental data with the smallest difference as the migration solu-tion;at the same time,the optimal solution set of the environment at time Performs crossover mutation to generate di-versity solutions,and perform non-dominated sorting with migration solutions to obtain source domain data;then map the source domain data to the embedding space,and obtain the optimal solution as the initial population in the new environment for the next iteration.operation.This method considers the reuse of multiple historical environmental knowledge,which can strengthen the global search ability of the population and effectively suppress the generation of negative migration,thereby improving the efficiency of the algorithm.Through experiments,the results show that the algo-rithm proposed in this paper can significantly improve the performance of dynamic multi-objective optimization methods.
维普
万方数据