Enhanced neighborhood search with knowledge transfer for multi-aircraft collaborative task allocation in emergency scenarios

Purpose This paper aims to propose a multi-start collaborative variable neighborhood search (VNS) method, named MCVNS, which effectively reuses and transfers informative knowledge among optimization cycles, to address the multi-aircraft collaborative task allocation in emergency scenarios. Design/methodology/approach The authors design a multi-start initialization strategy, using four distinct constructive heuristics to generate a variety of starting points for the VNS optimizers. Moreover, the authors design seven problem-specific local search operators tailored to enhance the effectiveness of these optimizers. To facilitate full collaboration among the VNS optimizers starting from different points, the authors develop a novel collaborative knowledge transfer strategy that efficiently reuses promising neighborhood sequences across different optimizers. Findings Comprehensive experimental studies on the public benchmark instances indicate that the proposed MCVNS significantly outperforms the state-of-the-art methods. Further analysis verifies the effectiveness of different devised components within MCVNS. Originality/value This paper demonstrates that reusing well-performing neighborhood sequences, previously explored by the VNS optimizers during the optimization process, can effectively guide subsequent optimization cycles and bring possible benefits.