Research on ship pipeline routing optimization algorithm based on improved artificial bee colony algorithm
-
摘要: 【目的】人工蜂群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时，存在容易陷入局部最优的缺陷。为解决船舶管道系统中的管路路径规划问题，提出一种改进的人工蜂群算法。【方法】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，并改进了侦查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。同时提出一种适应于解决分支管路路径寻优的人工蜂群协同进化算法。【结果】通过实例验证表明：改进后的人工蜂群算法与标准人工蜂群算法相比，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%。【结论】无论是解决单管路还是分支管路，改进后的人工蜂群算法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。Abstract: [Objectives] Artificial bee colony algorithm has the characteristics of few control parameters, strong local optimization ability and fast convergence speed. But it is easy to fall into local optimal solution. In order to solve the problem of pipeline routing in ship pipeline system, an improved artificial bee colony algorithm is proposed. [Methods]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 has been improved, from updating the points that the path passes to updating the "road sections" in the path. And this paper proposes an artificial bee colony co-evolution algorithm for solving the optimization of branch pipeline path. [Results] The example shows that
-
计量
- 文章访问数: 119
- 被引次数: 0
下载:
