| 523 | 0 | 121 |
| 下载次数 | 被引频次 | 阅读次数 |
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.
[1] 刘志超，石章松，姜涛，等.基于最小资源损耗的火力分配研究[J].火力与指挥控制，2018,43(6):167-170.LIU Z C,SHI Z S,JIANG T,et al.Research on method of weapon-target assignment based on the minimal resource loss[J].Fire control & command control,2018,43(6):167-170.
[2] 于博文，吕明.基于改进NSGA-III算法的动态武器协同火力分配方法[J].火力与指挥控制，2021,46(8):71-77,82.YU B W,LYU M.Method for dynamic weapon coordinative firepower distribution based on improved NSGA-Ⅲ algorithm[J].Fire control & command control,2021,46(8):71-77,82.
[3] 张明双，徐克虎.基于最小火力浪费的火力优化分配[J].电光与控制，2020,27(9):55-59.ZHANG M S,XU K H.Optimal firepower distribution based on minimum firepower waste[J].Electronics optics & control,2020,27(9):55-59.
[4] KONG L R,WANG J Z,ZHAO P.Solving the dynamic weapon target assignment problem by an improved multiobjective particle swarm optimization algorithm[J].Applied sciences,2021,11(19):9254.
[5] 王峰，张衡，韩孟臣，等.基于协同进化的混合变量多目标粒子群优化算法求解无人机协同多任务分配问题[J].计算机学报，2021,44(10):1967-1983.WANG F,ZHANG H,HAN M C,et al.Co-evolution based mixed-variable multi-objective particle swarm optimization for UAV cooperative multi-task allocation problem[J].Chinese journal of computers,2021,44(10):1967-1983.
[6] 聂俊峰，陈行军，苏琦.基于NSGA-Ⅲ算法的集群目标来袭火力分配建模与优化[J].兵工学报，2021,42(8):1771-1779.NIE J F,CHEN X J,SU Q.Modeling and optimization of weapon-target assignment for group targets defense based on NSGA-Ⅲ algorithm[J].Acta armamentarii,2021,42(8):1771-1779.
[7] MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in engineering software,2016,95:51-67.
[8] SUN Y J,CHEN Y.Multi-population improved whale optimization algorithm for high dimensional optimization[J].Applied soft computing,2021,112:107854.
[9] 郭振洲，王平，马云峰，等.基于自适应权重和柯西变异的鲸鱼优化算法[J].微电子学与计算机，2017,34(9):20-25.GUO Z Z,WANG P,MA Y F,et al.Whale optimization algorithm based on adaptive weight and cauchy mutation[J].Microelectronics & computer,2017,34(9):20-25.
[10] REN G,YANG R H,YANG R Y,et al.A parameter estimation method for fractional-order nonlinear systems based on improved whale optimization algorithm[J].Modern physics letters B,2019,33(7):1950075.
[11] JANGIR P,JANGIR N.Non-dominated sorting whale optimization algorithm (NSWOA):a multi-objective optimization algorithm for solving engineering design problems[J].Global journals of research in engineering,2017:17:15-42.
[12] 梁倩.基于反向精英保留和Levy变异的多目标鲸鱼优化算法[J].现代计算机，2021(18):25-31.LIANG Q.Multi-objective whale optimization algorithm based on reverse elite retention and Levy mutation[J].Modern computer,2021(18):25-31.
[13] SUN H,CAO A R,HU Z Y,et al.A novel quantile-guided dual prediction strategies for dynamic multi-objective optimization[J].Information sciences,2021,579:751-775.
[14] SONG Z H,LIU T,LIN Q Z.Multi-objective optimization of a solar hybrid CCHP system based on different operation modes[J].Energy,2020,206:118125.
[15] WANG Y K,WANG S L,LI D,et al.An improved multi-objective whale optimization algorithm for the hybrid flow shop scheduling problem considering device dynamic reconfiguration processes[J].Expert systems with applications,2021,174:114793.
[16] 孙海文，谢晓方，孙涛，等.改进型布谷鸟搜索算法的防空火力优化分配模型求解[J].兵工学报，2019,40(1):189-197.SUN H W,XIE X F,SUN T,et al.Improved cuckoo search algorithm for solving antiaircraft weapon-target optimal assignment model[J].Acta armamentarii,2019,40(1):189-197.
[17] WANG L,LIU X Y,SUN M H,et al.A new chaotic starling particle swarm optimization algorithm for clustering problems[J].Mathematical problems in engineering,2018,2018:1-14.
[18] 方冰，李太勇，吴江.一种基于网格划分的自适应粒子群优化算法[J].计算机应用研究，2010,27(11):4136-4139.FANG B,LI T Y,WU J.Grid-based adaptive particle swarm optimization[J].Application research of computers,2010,27(11):4136-4139.
[19] KNOWLES J D,CORNE D W.Approximating the nondominated front using the Pareto archived evolution strategy[J].Evolutionary computation,2000,8(2):149-172.
[20] DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE transactions on evolutionary computation,2002,6(2):182-197.
[21] COELLO C A,LECHUGA M S.MOPSO:a proposal for multiple objective particle swarm optimization[C]//Proceedings of the 2002 Congress on Evolutionary Computation.Piscataway:IEEE Press,2002:1051-1056.
[22] ISHIBUCHI H,MASUDA H,TANIGAKI Y,et al.Difficulties in specifying reference points to calculate the inverted generational distance for many-objective optimization problems[C]//IEEE Symposium on Computational Intelligence in Multi-criteria Decision-making.Piscataway:IEEE Press,2015:170-177.
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)