An energy optimal thrust allocation method for the marine dynamic positioning system based on adaptive hybrid artificial bee colony algorithm
An energy optimal thrust allocation method for the marine dynamic positioning system based on adaptive hybrid artificial bee colony algorithm
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DOI：
10.1016/j.oceaneng.2016.04.004
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发表时间：
2016-05
影响因子：
5
通讯作者：
Defeng Wu;Fengkun Ren;Weidong Zhang
中科院分区：
文献类型：
--
作者：
Defeng Wu;Fengkun Ren;Weidong Zhang
推力分配是动力定位系统的重要组成部分。TA的作用是分配每个推力器的推力和角度，使其达到所需的力和力矩。本文在前人研究的基础上，提出了一种混沌搜索自适应混合人工蜂群算法（AHABCC）。该算法将差分进化（DE）中的变异算子和粒子群优化（PSO）中的社会认知部分引入蜜蜂，将混沌搜索策略引入侦察兵搜索。动态调整所选搜索策略的比例，实现优化。因此，AHABCC可以自动切换不同蜂群的搜索策略。与HABCC相比，AHABCC的最优搜索速度更快，获得最优结果和避免局部最优的概率显著增加。此外，AHABCC的功耗比HABCC小。通过仿真验证了该算法的有效性。
Thrust allocation (TA) is an important part in dynamic positioning systems (DPS). The function of TA is to allocate the thrust and angle of each thruster so that the desired force and moment can be achieved. Based on our previous work, an adaptive hybrid artificial bee colony algorithm with chaotic search (AHABCC) is proposed in this study. This algorithm introduced a mutation operator from differential evolution (DE) and the social cognitive part of particle swarm optimization (PSO) to the honeybee and chaotic search strategies to scouts searching. The proportion of each search strategy selected is dynamically adjusted to achieve the optimization. Therefore, the AHABCC can automatically switch the search strategy for different bee colonies. The optimal search of AHABCC is faster compared to HABCC, and the probability of obtaining optimal results and avoiding local optimums is significantly increased. In addition, the power consumption of AHABCC is less than that of HABCC. The effectiveness of the AHABCC algorithm is demonstrated using simulations.