基于蚁群算法和极限学习机的舰船电子装备备件优化模型

Optimization model of ship electronic equipment spare parts based on ant colony algorithm and limit learning machine

根据分析得到的舰船电子装备使用特点,创建包含定可靠度备件优化和定费用备件优化两部分的舰船电子装备备件优化模型,以可靠度与费用的比值作为目标函数,使用蚁群算法改进极限学习机求解所建模型,将极限学习机的初始权值和阈值当作蚁群算法内各蚂蚁的爬行路径节点,通过最佳路径搜索获得全局最优解,实现舰船电子装备备件优化。实验结果表明:模型求解所得结果的多样性较高,且舰船可靠度一定时,该方法的库存备件总费用始终保持最低;该方法能有效保证电子装备在整个舰船航行期间的随时可用程度。

According to the use characteristics of ship electronic equipment obtained from the analysis, a spare parts optimization model of ship electronic equipment is established, which includes two parts: fixed reliability spare parts optimization and fixed cost spare parts optimization. Taking the ratio of reliability to cost as the objective function, the ant colony algorithm is used to improve the limit learning machine to solve the model, taking the initial weight and threshold of the limit learning machine as the crawling path node of each ant in the ant colony algorithm, the global optimal solution is obtained through the optimal path search to realize the optimization of ship electronic equipment spare parts. The experimental results show that when the diversity of the results obtained from the model is high and the ship reliability is certain, the total cost of spare parts in stock is always kept at the lowest;This method can effectively ensure the availability of electronic equipment at any time during the whole ship navigation.