| 局部搜索灰狼优化算法求解武器-目标分配问题 |
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| 引用本文： | 杨玉,张嘉佳,马金慧,徐子瑞,戴红伟.局部搜索灰狼优化算法求解武器-目标分配问题[J].科学技术与工程,2023,23(27):11722-11729. |
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| 作者姓名： | 杨玉 张嘉佳 马金慧 徐子瑞 戴红伟 |
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| 作者单位： | 江苏海洋大学计算机工程学院 连云港 222000 |
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| 基金项目： | 国家自然科学基金资助项目(61873105) |
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| 摘 要： | 武器—目标分配（Weapon Target Assignment, WTA）问题是根据武器对来袭目标毁伤概率的不同，合理确定待打击目标的武器分配方案，以达到尽可能少的武器对来袭目标毁伤程度最大化的目的，是作战指挥决策领域的重要研究内容。在构建WTA问题模型的基础上，针对传统灰狼优化（Grey Wolf Optimization, GWO）算法局部开发能力不足等问题，采取了一种精英保留及免疫变异局部搜索策略。改进灰狼优化算法（Improved Grey Wolf Optimization, IGWO）首先在灰狼种群中选择部分优质精英个体，然后通过随机点变异和受体编辑两种免疫局部搜索策略对精英个体进一步寻优，从而改善传统GWO算法过早收敛和易陷入局部最优的缺点。针对3种不同规模的武器—目标分配问题，将IGWO与交叉熵算法、传统GWO算法进行了对比，计算结果显示IGWO算法所求适应度值的分位数均明显高于对比算法，进而验证了IGWO算法的有效性。
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| 关 键 词： | 武器—目标分配 灰狼算法 精英保留 免疫变异 局部搜索 |
| 收稿时间： | 2023/2/8 0:00:00 |
| 修稿时间： | 2023/9/11 0:00:00 |
Local Search Strategy Based Grey Wolf Optimization for Weapon Target Assignment Problem |
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| Yang Yu,Zhang Jiaji,Ma Jinhui,Xu Zirui,Dai Hongwei.Local Search Strategy Based Grey Wolf Optimization for Weapon Target Assignment Problem[J].Science Technology and Engineering,2023,23(27):11722-11729. |
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| Authors: | Yang Yu Zhang Jiaji Ma Jinhui Xu Zirui Dai Hongwei |
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| Affiliation: | School of Computer Engineering,Jiangsu Ocean University,Lianyungang,222000;China |
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| Abstract: | Weapon Target Assignment (WTA) problem is an important problem in the field of combat command and decision-making, which aims to determine a reasonable weapon allocation scheme for the target to be attacked based on the different damage probability of the weapon to the incoming target, in order to achieve the goal of minimizing the damage degree of the weapon to the incoming target... Based on the construction of WTA problem model, an elite retention and immune mutation local search strategy was adopted in grey wolf optimization (GWO) to improve the local search ability of the traditional GWO algorithm. The improved Grey Wolf Optimization (IGWO) firstly selects some high-quality elite individuals from the gray wolf population, and then further optimizes the elite individuals by using two immune local search strategies, random point mutation and receptor editing, to further optimize the elite individuals, thereby improving the shortcomings of premature convergence and easy to fall into local optimization of traditional GWO algorithms.. Aiming at three scales of WTA problems, IGWO is compared with the cross entropy algorithm and traditional GWO algorithm, the calculation results show that the quantiles of fitness values obtained by the IGWO algorithm are significantly higher than those of the comparison algorithm, thereby verifying the effectiveness of the IGWO algorithm. |
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| Keywords: | weapon target assignment grey wolf optimization elite retention immune mutation local search |
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