针对航空母舰飞行甲板上舰载机弹药保障面临的调度效率不高的问题，提出了一种改进灰狼优化(grey wolf optimizer，GWO)算法。根据甲板上多升降机多运输车的场景特点，建立了由多车场出发、向多 目标转运的问题模型。融合遗传算法算子交叉思想实现了对灰狼种群初始解的初步优化，并通过直线转运路径中间点定义、整数编码、负整数标志分组等方法实现了对GWO算法求解过程的改进。同时，增加了灰狼个体自由狩猎流程，有效克服了结果陷入局部最优和早熟的问题。最终，通过对场景实例的优化求解，验证了所提方法的有效性和可行性。
Carrier-based aircraft ammunition support scheduling optimization based on improved grey wolf optimizer algorithm
An improved grey wolf optimizer(GWO)algorithm is proposed to solve the problem of inefficient scheduling faced by carrier-based aircraft ammunition support on the flight deck of aircraft carriers.According to the characteristics of the scenario of multiple lifts and multiple transport vehicles on the deck,the problem of transferring from multiple vehicle fields to multiple targets is modeled.The initial optimization of the initial solution of the grey wolf population is achieved by integrating the idea of genetic algorithm operator crossover,and the improvement of the solution process of the GWO algorithm is achieved by linear path transportation midpoint definition,integer encoding and negative integer sign grouping,etc.At the same time,the free hunting process of individual grey wolf is also added to effectively overcome the problem of results falling into local optimum and prematureness.Finally,the effectiveness and feasibility of the proposed method are verified through the optimal solution of the scenario example.
grey wolf optimizer(GWO)algorithmmultiple vehicle fieldsmultiple objectivesinteger encodingsign grouping
万方数据
维普
