Wei Na, Liu Mingyong, Cheng Weibin
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Well, Xi'an Shiyou University, Xi'an 710065, China.
Sensors (Basel). 2019 May 13;19(9):2211. doi: 10.3390/s19092211.
This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.
本文基于多目标决策理论提出了一种水下对抗多目标决策模型,并使用多目标离散粒子群优化(MODPSO)算法对其进行求解。现有的决策模型基于完全分配任务,未考虑武器消耗和通信延迟,这与实际海战过程不符。本文选择最小敌方残余威胁概率和最小己方武器消耗作为多目标决策模型的两个函数。考虑到通信延迟的影响,提出了多目标离散粒子群优化(MODPSO)算法以获得不同武器消耗下分配方案的最优解。该算法采用自然数编码方法,粒子对应对抗策略。仿真结果表明水下通信延迟会影响决策选择。验证了所提模型和所提多目标离散粒子群优化算法的有效性。