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传统多目标优化算法在解决多于两个目标函数的火力分配问题时收敛效果不佳，多样性差，耗时过大。基于此，提出了一种自适应网格多目标鲸鱼优化算法(AG-MOWOA)来解决以震塌比例、弹药成本和自身剩余价值为目标函数的火力分配问题。该算法引入混沌映射和外部Pareto存档进化策略提高了种群的多样性，通过自适应网格选取最优个体的方法极大地减少了算法运行时间。仿真实验结果表明，该算法较其他算法收敛速度更快、收敛质量更高、解集分布更多样，能够有效解决火力分配问题。
Abstract：The traditional multi-objective optimization algorithm had poor convergence effect, bad diversity and serious time-consuming problems when solving the firepower assignment problem with more than two objective functions. Based on this situation, an adaptive grid multi-objective whale optimization algorithm(AG-MOWOA) was proposed. The algorithm was to solve the firepower assignment optimization problem in which the collapse ratio, ammunition cost, and the own surplus value were taken as the objective functions. Besides, the algorithm increased the population diversity by introducing chaotic mapping and using external Pareto archival evolutionary strategy, and reduced the algorithm running time greatly by selecting the optimal individuals through adaptive grid. The results of the simulation experiments showed that the algorithm had better convergence speed and stability, and more diverse solution set distributions than other traditional algorithms in solving the firepower assignment problem.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2023010
中图分类号:TP18;E91
引用信息:
[1]佘维,王业腾,孔德锋等.基于自适应网格多目标鲸鱼算法的火力分配问题研究[J].郑州大学学报(理学版),2024,56(06):17-24.DOI:10.13705/j.issn.1671-6841.2023010.
基金信息:
科技部国家重点研发计划课题(2020YFB1712401);; 河南省重点研发与推广专项(212102310039,202102310554);; 河南省高等学校重点科研项目(20A520035)