基于两层知识迁移的多代理多任务优化方法

Multi-surrogate Multi-task Optimization Approach Based on Two-layer Knowledge Transfer

进化多任务优化是计算智能领域一个新兴的研究方向,它致力于研究通过进化算法如何同时、有效地求解多个优化问题,从而提高单独求解每个任务的性能。基于此,提出了一种基于两层知识迁移的多代理多任务优化算法(AMS-MTO),其通过在代理间和代理内同时进行知识迁移来达到跨域优化的目的。具体来讲,代理内的知识迁移是通过差分进化实现决策变量信息的跨维迁移,从而避免算法陷入局部最优;代理间的学习采用了隐式知识迁移和显式知识迁移两种策略。隐式知识迁移利用种群的选择性交叉来产生后代,促进遗传信息的交流;显式知识迁移是对精英个体的迁移,可以弥补隐式迁移随机性很强的缺点。为了评估两层知识迁移的多代理多任务优化方法的有效性,在8个高达100维的基准问题上进行了实证研究,同时给出了收敛证明,并将其与现有的算法进行了对比。实验结果表明,在求解单目标优化的昂贵问题时,AMS-MTO算法效率更高,性能更好,收敛速度更快。

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.