﻿ 绿色能源双体无人艇艇型综合优化分析
 舰船科学技术  2021, Vol. 43 Issue (3): 102-106    DOI: 10.3404/j.issn.1672-7649.2021.03.020 PDF

Comprehensive optimization analysis of green energy catamaran unmanned craft
SHI Yan, YANG Song-lin, HUANG Xiao-yu, KE Wei-shun, ZHANG Jin-cheng
Shipping and Marine Engineering Institute Jiangsu University of Science and Technology, Zhenjiang 212000, China
Abstract: In order to achieve the optimal design of the green energy catamaran unmanned craft, this paper studies the multi-objective strategy and intelligent optimization algorithm of unmanned craft type design. Firstly, a comprehensive optimization mathematical model is established by comprehensively considering the influence of solar energy and sail and the four performances of fastness, maneuverability, wave resistance and overturning resistance on the design of the craft type;Then the unmanned craft comprehensive optimization software based on genetic algorithm was used to determine the genetic times, population size, mutation probability and crossover probability under the optimal condition of the total objective function;Finally, the external hierarchical strategy was used to compare and analyze the hybrid algorithm combining genetic algorithm with particle swarm optimization and chaos algorithm.The results show that: compared with the single genetic algorithm, the hybrid algorithm has better optimization effect. In the case of different carrier probability, the optimization effect of the genetic algorithm + particle swarm algorithm is the best. The external layering strategy can effectively improve the optimization effect.
Key words: catamaran unmanned craft     green energy     unmanned craft type design     layering strategy     carrier probability
0 引　言

1 综合优化数学模型 1.1 设计变量

1.2 目标函数

 $Q(x) = {Q_1}{(x)^{\alpha 1}} \times {Q_2}{(x)^{\alpha 2}} \times {Q_3}{(x)^{\alpha 3}} \times {Q_4}{(x)^{\alpha 4}} \times {Q_5}{(x)^{\alpha 5}}{\text{。}}$ (1)

 ${Q_1}\left( x \right) = \frac{{{V^3}{\Delta ^{2/3}}}}{{{P_S}}} = \frac{{{V^3}{\Delta ^{2/3}}{\eta _H}{\eta _O}{\eta _S}{\eta _R}}}{{{P_E}}}\text{。}$ (2)

 ${Q_2}\left( x \right) = C' = {Y_\nu }'{N_r}' - {N_v}'({Y_r}' - {m'})\text{。}$ (3)

 ${Q_3}\left( x \right) = \mu = \frac{N}{{\sqrt {{{I'}_{xx}}\Delta GM} }}\text{。}$ (4)

 ${Q_4}(x) = G{M^{{\eta _1}}}\times\overline {GM} _1^{{\eta _2}}\text{。}$ (5)

 ${Q_5}\left( x \right) = S = \frac{{24\times{P_S}/4.5}}{{1000 \times 0.18}} = 0.03{P_S}\text{。}$ (6)
1.3 约束条件

1）等式约束

 $\Delta = 2\rho LBT{C_b}\text{；}$ (7)

 $\frac{{{\eta _R}{\eta _s}{P_s}}}{{2\pi N}} = {K_Q}\rho {N^2}D_p^5\text{；}$ (8)

 ${R_t} = {T_t} + 2{N_p}{K_T}\rho {N^2}{D_p}^4(1 - t)\text{。}$ (9)

2）不等式约束

 $(1.3 + 0.3Z){T_e}/(({P_0} - {P_V})D_P^2) + K - {A_{eo}} \leqslant 0\text{；}$ (10)

 $GM > 0.3\text{；}$ (11)

 ${T_1} < {H_1} + {H_2}\text{；}$ (12)

 $G{M_1} > 0\text{；}$ (13)

 $S < 1.2L{B_O}\text{；}$ (14)

 ${\theta _1} < {12^\circ }\text{。}$ (15)
2 遗传算法的优化计算与分析

 图 1 遗传算法的运算过程 Fig. 1 Operation process of genetic algorithm

3 混合算法的比较分析

 图 2 不同载波概率下的优化算法对比 Fig. 2 Comparison of optimization algorithms under different carrier probably
4 结　语

1）总目标函数值基本随着遗传次数、种群规模的增加而变大，并最终趋于稳定值。由于遗传次数和种群规模过大时，会导致优化计算时间过长且不一定能得到更好的结果，所以有必要寻求最优值。对于本文的优化数学模型而言，当遗传次数为7000、种群规模为500时，总目标函数值已达到最大值，说明此时的优化效果已经最佳。

2）不同的变异概率和交叉概率对于总目标函数值存在不同影响。本文中当交叉概率为0.9时，总目标函数值最大，此时能达到更大的解空间，而变异概率为0.04时，优化结果最优，说明应在种群中适量导入新的基因。

3）相比于单一遗传算法，混合算法的优化效果更好，且在不同载波概率情况下，遗传算法+粒子群算法的优化效果均为最佳，外部分层策略可以有效提高寻优效果。

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