Treffer: A pre-communication mechanism for evolutionary multitasking optimization.

Evolutionary multitasking optimization (EMTO) aims to make use of evolutionary algorithms to solve multiple optimization tasks simultaneously through exploiting the relevant information between different tasks. However, the existing EMTO algorithms (EMTOAs) usually begin the optimization process from scratch without considering the prior information between different tasks, which would restrict the solving performance. This paper proposes a pre-communication mechanism (PCM) for EMTO, which takes the distribution information of the initial population corresponding to each task as the prior information, and uses the correlation of the prior information to provide refined solutions for each task in the each generation of the early evolution. Firstly, after generating initial individual solutions, PCM constructs a Gaussian distribution model on the individual solutions of each task as the prior information. Next, in the each generation of the early evolution, PCM takes each task as the target task in turn and learns the similarity information between the target task and other tasks by constructing a Gaussian mixture model. Mixture coefficients represent the learned similarity information between the target task and other tasks. Finally, the individual solutions sampled from the obtained Gaussian mixture model of each task compete with original individual solutions of each generation of early evolution to obtain refined individual solutions. The experimental results show that PCM can help the existing EMTOAs to solve multitasking optimization problems more effectively and efficiently. [ABSTRACT FROM AUTHOR]