Abstract:

Operation loop recommendation (OLR) is based on the optimization algorithm to recommend the optimal operation loop from the combat network for the commander, in order to strike the target with high quality. The OLR in the future operations is faced with the characteristics of large scale and fast decision-making pace. In this regard, an integrated improved ant colony (AC) algorithm is proposed, which can realize efficient and high-quality optimization solution on OLR. Firstly, the OLR problem is transformed into a mathematical model based on multi-warehouse path planning. Secondly, to solve the problems of the original AC algorithm, such as slow convergence speed in the early stage, the algorithm parameters have great influence on the results, and easy to fall into the local optimization, three improvement strategies are proposed and integrated: pheromone initialization based on edge weight information, adaptive optimization of AC algorithm parameters based on differential evolution, and improvement of global search ability based on genetic operator. Finally, the case study analyzes and compares the integrated improved AC algorithm, verifies that the optimization result of the proposed algorithm is better than that of the unintegrated improved AC algorithm without significantly increasement of the time consumption, and the effect is significantly improved compared with the original AC algorithm.

Key words: operation loop recommendation (OLR), multi-warehouse path planning, intelligent optimization, ant colony (AC) algorithm, integrated improvement