Multitasking differential evolution with difference vector sharing mechanism

As a new emerging research topic in the field of evolutionary computation, evolutionary multitasking optimization (EMTO) is presented to solve multiple optimization tasks concurrently by transferring knowledge across them. However, the promising search directions found during the evolutionary process have not been shared and utilized effectively in most EMTO algorithms. Therefore, this paper puts forward a difference vector sharing mechanism (DVSM) for multitasking differential evolution (MDE), with the purpose of capturing, sharing and utilizing the useful knowledge across different tasks. The performance of the proposed algorithm, named MDE with DVSM (MDE-DVSM), is evaluated on a suite of single-objective multitasking benchmark problems. The experimental results have demonstrated the superiority of MDE-DVSM when compared with other competitive algorithms.