Abstract
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.
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