Learning-imitation strategy-assisted alpine skiing optimization for the boom of offshore drilling platform

Abstract

The boom of a semi submersible platform has large moment of inertia and high operating cost. How to effectively achieve the optimal solution of the boom to minimize the design and operation cost is a dynamic problem. In this study, a learning-imitation strategy-assisted alpine skiing optimization (LISASO) is proposed to find the optimal solution of the semi submersible platform boom. Firstly, the optimization model of the boom of the semi submersible platform is established. Secondly, the learning-imitation strategy (LIS) is implemented to improve the performance of the alpine skiing optimization (ASO). In LIS, the learning ability of individuals and the imitation of competitions are introduced to strengthen the association between individuals and the first individual. The performance of the LISASO is verified by three truss examples. The statistical results demonstrate that the LISASO is more competitive compared with other state-of-the-art optimization algorithms. Finally, the LISASO is applied to solve the optimal structural parameters of the boom. Results show that the energy consumption is reduced by 18.32% compared with the initial design.