Decision-Making of Underwater Cooperative Confrontation Based on MODPSO
Decision-Making of Underwater Cooperative Confrontation Based on MODPSO
复制标题
基于MODPSO的水下协同对抗决策
DOI：
10.3390/s19092211
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发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
Mingyong Liu
中科院分区：
文献类型：
--
作者：
Na Wei;Mingyong Liu
基于多目标决策理论，提出了水下对抗的多目标决策模型，并采用多目标离散粒子群优化算法进行求解。现有的决策模型都是基于完全分配分配，没有考虑武器消耗和通信延迟，不符合实际的海战过程。选择最小对手剩余威胁概率和最小自身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算法（MODPSO），以获得不同武器消耗下的分配方案的最优解。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，水下通信延迟影响决策选择。验证了所提模型和多目标离散粒子群优化算法的有效性。
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.
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DOI：
10.1109/cec.2015.7257280
发表时间：
2015-05
期刊：
2015 IEEE Congress on Evolutionary Computation (CEC)
影响因子：
--
作者：
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通讯作者：
Juan Li;Jie Chen;Bin Xin;L. Dou
DOI：
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发表时间：
2018-02-01
期刊：
Sensors (Basel, Switzerland)
影响因子：
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期刊：
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影响因子：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
2015
期刊：
Systems engineering and electronics
影响因子：
--
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