Abstract: Evolutionary multi-task optimization is a new research direction in the field of computational intelligence.It focuses on how to handle multiple optimization tasks effectively and simultaneously through evolutionary algorithm,so as to enhance the performance of solving each task individually.Based on this,a multi-surrogate and multi-task optimization approach based on two-layer knowledge transfer(AMS-MTO) is proposed,which achieves the purpose of cross-domain optimization by transferring knowledge between surrogates and within surrogates at the same time.Specifically,the knowledge transfer within the surrogates realizes the cross-dimensional transfer of decision variable information through differential evolutionary,so as to avoid the algorithm falling into local optimum.The learning between surrogates adopts two strategies:implicit knowledge transfer and explicit knowledge transfer.The former uses the selective crossover of populations to generate offspring and promote the exchange of genetic information.The latter is mainly the transfer of elite individuals,which can make up for the strong randomness of implicit transfer.For the sake of evaluate the effectiveness of the AMS-MTO algorithm,we carry out an empirical study on 8 benchmark problems up to 100 dimension.At the same time,we give the convergence proof and compare it with the existing algorithms.Experiment resultsshow that when solving expensive problems of single objective optimization,the AMS-MTO algorithm has higher efficiency,better performance and faster convergence speed.

Key words: Evolutionary multi-task optimization, Multi-surrogate, Knowledge transfer, Elite individuals, Implicit transfer