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舰载备件配备方案的选定受到舰船存储空间限制和装备当前状态影响。以动态规划优化方案为研究背景,在分析方案影响因素的基础上,结合装备维修策略与已工作时间,建立了与装备当前状态相关的优化模型。针对所建模型,在研究标准粒子群算法(PSO)的基础上,将模型约束条件转化为多目标优化,并引入改进的多群PSO解决了算法的动态适应问题,设计了多群多目标PSO算法。以规划舰载电子对抗设备的备件配备方案为例,对基于该算法的舰载备件配备方案进行了实验验证。实验结论表明,该算法适用于此类问题的求解,寻优效果明显高于标准粒子群算法。
The selection of carrier-borne spare parts provision programs is limited by the ship’s storage space constraints and the current state of equipment. Taking the dynamic programming optimization scheme as the research background, based on the analysis of the influencing factors of the scheme, combined with the equipment maintenance strategy and working hours, an optimization model related to the current state of the equipment is established. Based on the study of the standard Particle Swarm Optimization (PSO), the model constraints are transformed into multi-objective optimization, and the improved multi-group PSO is introduced to solve the problem of dynamic adaptation of the algorithm. Multi-target multi-group PSO algorithm. Taking the planning of spare parts allocation of shipborne electronic countermeasures equipment as an example, the experimental verification of the carrier spare parts allocation plan based on this algorithm is carried out. Experimental results show that this algorithm is suitable for solving such problems, and the optimization effect is obviously higher than the standard particle swarm optimization algorithm.