Neutrosophic Sets and Systems
Abstract
the improvement degree in the new solution, which causes fuzziness and uncertainty in the operator score. To solve the aforementioned problem, the main innovation of this study is to propose an adaptive neutrosophic large neighborhood search (ANLNS). Specifically, the main work is as follows. Firstly, the number of times each operator scores are quantified by constructing NSs, thereby analyzing algorithm performance and preventing the idealization of scores. Secondly, a novel neutrosophic utility function and score function are proposed to assign an appropriate score for the operator within a reasonable interval. Finally, the effectiveness and robustness of the proposed ANLNS is validated by the capacitated vehicle routing problem benchmarks with three varying scales and comparative analyses. The compared results indicate that the proportion of best solutions for ANLNS are 50%, 100%, and 37.5%, which significantly highlight the robustness and reliability of the proposed algorithm when the degree of destruction is 0.3, 0.5, and 0.7, respectively. Meanwhile, the proposed ANLNS is efficient and flexible, providing a novel method for addressing other situation optimization problems.
Recommended Citation
Jiang, Jun; Fangwei Zhang; Yiying Pan; Rui Wang; and Zhu Han. "An adaptive neutrosophic large neighborhood search and its application." Neutrosophic Sets and Systems 70, 1 (2024). https://digitalrepository.unm.edu/nss_journal/vol70/iss1/2
