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《中国舰船研究》近日在线发表了2024年第2期英文翻译版文章，题为
Analysis of ship pipeline routing optimization algorithm based on improved artificial bee colony algorithm
基于改进人工蜂群算法的船舶管路路径寻优算法分析
The artificial bee colony (ABC) algorithm has such characteristics as few control parameters, strong local optimization ability and fast convergence speed. However, when solving path optimization problems, it can easily fall into local optimal solutions. In order to solve the problem of pipeline routing in a ship pipeline system, an improved artificial bee colony (IABC) algorithm is proposed.
Based on the traditional artificial bee colony algorithm, the crossover operation of genetic operators is introduced into the update mechanism of following bees, and an adaptive strategy is adopted for the crossover probability of the crossover operator. The crossover operation on the population is used to find new solutions in the global range. The way scout bees search for new paths is improved from updating the points that the path passes to updating the "road sections" in the path. This paper proposes an artificial bee colony co-evolution algorithm for solving the optimization of branch pipeline paths.
Compared with the standard artificial bee colony algorithm, the improved algorithm can improve the path layout effect by 32.3%–37.4% and the convergence speed by 17.7%–29.9%.
The improved artificial bee colony algorithm proposed herein has higher solution quality, faster convergence speed and better stability than the traditional artificial bee colony algorithm for a single pipe or branch pipe.
人工蜂群（ABC）算法具有控制参数少、局部寻优能力强、收敛速度快的特点，但在解决路径寻优问题方面，存在容易陷入局部最优的缺陷。为解决船舶管路系统中的管路路径规划问题，提出一种改进的人工蜂群（IABC）算法。
在传统人工蜂群算法的基础上，在跟随蜂的更新机制中引入遗传算子中的交叉操作，并对交叉算子的交叉概率采用自适应的策略；通过对种群进行的交叉操作寻找全局范围内的新解，并改进侦察蜂寻找新路径的方式，由原来的对路径经过的点进行更新改为对路径中的“路段”进行更新；随后，提出一种适应于解决分支管路路径寻优的改进人工蜂群协同进化算法。
实例验证表明，改进后的人工蜂群算法相比标准人工蜂群算法其路径布置效果能够提升32.3%～37.4%，收敛速度能够提升17.7%～29.9%。
无论是解决单管路还是分支管路，改进后的人工蜂群算法相比传统的人工蜂群算法求解质量更高、收敛速度更快、稳定性更好。
基于人工蜂群的协同进化算法的分支管路路径寻优流程图
往期英文文章回顾：
1. Tracking control of ships based on ADRC−MFAC/船舶自抗扰无模型自适应航迹控制
2. Optimal obstacle avoidance design of autonomous underwater vehicle for cable laying based on model prediction control/基于模型预测的海缆铺设用水下航行器最优避障控制
3. Optimal trajectory tracking control of unmanned surface vehicle formation under unknown disturbances/未知扰动下的无人艇编队优化轨迹跟踪控制
4.Unmanned ship heading tracking control strategy with state quantization and input quantization/带有状态/输入量化的无人艇航向跟踪控制
编辑部整理了2019年以来出版的英文双语论文，每篇均对应出版了中英文两种语言版本，并按专业进行了分类。请点击“2019年以来双语版论文汇总”查看。
中国舰船研究
Chinese Journal of Ship Research
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