Multi-Population Evolutionary Algorithm via Seed Transfer for Multitasking Traveling Salesman Problem

Evolutionary multitasking optimization (EMTO) has attracted much attention in the community of evolutionary computation, which solves multiple tasks simultaneously by exchanging information between tasks. Multitasking traveling salesman problem (MTSP) is one of the most important combinatorial optimization problems in EMTO. However, redundant encoding and inefficient probabilistic transfer mechanisms used in most existing works may lead to negative transfer. In this paper, a new multi-population evolutionary algorithm via seed transfer (MPEA-ST) is proposed for MTSP. Firstly, combining heuristics with EMTO, a new seed encoding strategy, and seed growth mechanism are proposed to overcome redundant coding and suppress negative transfer. Moreover, a new seed selection mechanism and transfer strategy are designed to select seeds with knowledge. Finally, a new dataset construction method is developed to address the lack of MTSP benchmarks with different similarities. Experimental results show the superiority of the proposed MPEA-ST compared to other state-of-the-art methods on synthetic datasets and real-world datasets.