Raw data for the article "An improved genetic-simulated annealing algorithm with domain knowledge for efficient ballast water dynamic allocation of revolving floating cranes"

The ballast water dynamic allocation optimization is crucial for the safety operation of revolving floating cranes (RFCs). However, the solution of ballast water dynamic allocation scheme has the limitation of slow solution speed, which makes it difficult to apply in the engineering practice. To overcome this, a fuzzy genetic-simulated annealing algorithm (FGSAA) is proposed by integrating domain knowledge. First, a dynamic programming-based solution strategy is used to establish an optimization model for the ballast water dynamic allocation. Domain knowledge of ballast water allocation is then extracted to establish a fuzzy inference system for selecting ballast tanks to conduct the ballast water allocation. Based on the ballast tanks selected by the fuzzy inference system, an enhanced genetic-simulated annealing algorithm is used to solve the allocation scheme. Finally, numerical experiments with different crane loads and different quantities of ballast tank are conducted to verify the feasibility and reliability of this FGSAA. The results show that the proposed FGSAA method is feasible and reliable, which enriches the theory for the intelligent of RFC and provides the reference for other engineering problem.