基于多群多目标粒子群算法的舰载备件配备方案(英文)

Shipborne Spare Parts Support Scheme Based on Multi-group and Multi-objective Particle Swarm Optimization

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。

Selection for shipborne spare parts outfitting schemes was influenced by ship store space and current states of equipments. Based on studying scheme influence factors, prioritization model related to current states of equipments under the background of dynamic programming was established, which combined equipment’s maintenance strategy with its worked time. Aiming at the model, a new multi-group and multi-objective PSO（Particle Swarm Optimization） was designed and implemented, which transformed constraint conditions of the model into multi-objective problem, and solved dynamicadaptive problems via modified multi-group PSO. Shipborne spare parts support scheme of electronic countermeasures equipment was taken, and the validity of the multi-group and multi-objective PSO was tested. Conclusion shows that the algorithm is suitable for this type of problem, and has better effect than traditional PSO.