College of Electronic Engineering, National University of Defense Technology, Hefei, China.
Third Interdisciplinary Center (HeFei), National University of Defense Technology, Hefei, China.
PLoS One. 2021 Jun 9;16(6):e0252293. doi: 10.1371/journal.pone.0252293. eCollection 2021.
In modern warfare, the comprehensiveness of combat domain and the complexity of tasks pose great challenges to operational coordination.To address this challenge, we use the improved triangular fuzzy number to express the combat mission time, first present a new multi-objective operational cooperative time scheduling model that takes the fluctuation of combat coordinative time and the time flexibility between each task into account. The resulting model is essentially a large-scale multi-objective combinatorial optimization problem, intractably complicated to solve optimally. We next propose multi-objective improved Bat algorithm based on angle decomposition (MOIBA/AD) to quickly identify high-quality solutions to the model. Our proposed algorithm improves the decomposition strategy by replacing the planar space with the angle space, which helps greatly reduce the difficulty of processing evolutionary individuals and hence the time complexity of the multi-objective evolutionary algorithm based on decomposition (MOEA/D). Moreover, the population replacement strategy is enhanced utilizing the improved bat algorithm, which helps evolutionary individuals avoid getting trapped in local optima. Computational experiments on multi-objective operational cooperative time scheduling (MOOCTS) problems of different scales demonstrate the superiority of our proposed method over four state-of-the-art multi-objective evolutionary algorithms (MOEAs), including multi-objective bat Algorithm (MOBA), MOEA/D, non-dominated sorting genetic algorithm version II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO). Our proposed method performs better in terms of four performance criteria, producing solutions of higher quality while keeping a better distribution of the Pareto solution set.
在现代战争中，作战领域的综合性和任务的复杂性给作战协调带来了巨大的挑战。为了应对这一挑战，我们使用改进的三角模糊数来表示作战任务时间，首先提出了一种新的多目标作战协同时间调度模型，该模型考虑了作战协调时间的波动和各任务之间的时间灵活性。所提出的模型本质上是一个大规模的多目标组合优化问题，很难最优地解决。接下来，我们提出了基于角度分解的多目标改进蝙蝠算法（MOIBA/AD），以快速识别模型的高质量解决方案。我们提出的算法通过用角度空间代替平面空间来改进分解策略，这有助于大大降低处理进化个体的难度，从而降低基于分解的多目标进化算法（MOEA/D）的时间复杂度。此外，利用改进的蝙蝠算法增强了种群替换策略，帮助进化个体避免陷入局部最优。对不同规模的多目标作战协同时间调度（MOOCTS）问题的计算实验表明，我们提出的方法优于四种最先进的多目标进化算法（MOEAs），包括多目标蝙蝠算法（MOBA）、MOEA/D、非支配排序遗传算法 II（NSGA-II）和多目标粒子群优化算法（MOPSO）。在四个性能标准方面，我们提出的方法表现更好，在保持 Pareto 解集更好分布的同时，生成更高质量的解决方案。
