Multi-objective optimization of operation loop recommendation for kill web

In order to improve our military 's level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e., "web-based kill," and the operation loop theory. Firstly, we pio-neer the operation loop recommendation problem with opera-tion ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model. Secondly, considering the case where there are multiple deci-sion objectives for the combat ring recommendation problem, we propose for the first time a multi-objective optimization algo-rithm, the multi-objective ant colony evolutionary algorithm based on decomposition (MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition (MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives non -dominated solution set for the multi-objective problem. Finally, compared with other classical multi-objective optimization algo-rithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume (HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders' decision-making.