Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGA-II and adaptive MOEA/D: A comparison study
Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGA-II and adaptive MOEA/D: A comparison study
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DOI：
10.1109/cec.2015.7257280
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Juan Li;Jie Chen;Bin Xin;L. Dou
中科院分区：
文献类型：
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作者：
Juan Li;Jie Chen;Bin Xin;L. Dou
武器目标分配（WTA）问题是运筹学在国防领域应用中的一个基本问题，而多阶段武器目标分配（MWTA）问题是实际中普遍存在的动态武器目标分配（DWTA）问题的基础。本文所考虑的MWTA问题是一个具有两个竞争目标的多目标约束组合优化问题。在考虑资源约束、可行性约束和火力转移约束的条件下，除了对敌方目标的毁伤最大化外，本文遵循弹药消耗最小化的原则。为了应对这两个挑战，两种类型的多目标优化器：NSGA-II（基于支配）和MOEA/D（基于分解）增强自适应机制，以实现有效的问题求解。在此基础上，对自适应NSGA-II（ANSGA-II）和自适应MOEA/D（AMOEA/D）算法在三尺度MWTA问题上的求解实例进行了比较研究，并采用四种性能指标对两种算法进行了评价。数值结果表明，ANSGA-II在求解多目标MWTA问题上优于AMOEA/D，自适应机制明显提高了两种算法的性能。
The weapon target assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research, and the multi-stage weapon target assignment (MWTA) problem is the basis of dynamic weapon target assignment (DWTA) problems which commonly exist in practice. The MWTA problem considered in this paper is formulated into a multi-objective constrained combinatorial optimization problem with two competing objectives. Apart from maximizing damage to hostile targets, this paper follows the principle of minimizing ammunition consumption under the consideration of resource constraints, feasibility constraints and fire transfer constraints. In order to tackle the two challenges, two types of multi-objective optimizers: NSGA-II (domination-based) and MOEA/D (decomposition-based) enhanced with an adaptive mechanism are adopted to achieve efficient problem solving. Then a comparison study between adaptive NSGA-II (ANSGA-II) and adaptive MOEA/D (AMOEA/D) on solving instances of three scales MWTA problems is done, and four performance metrics are used to evaluate each algorithm. Numerical results show that ANSGA-II outperforms AMOEA/D on solving multi-objective MWTA problems discussed in this paper, and the adaptive mechanism definitely enhances performances of both algorithms.