Find in Library

Slime mould algorithm (SMA) and arithmetic optimization algorithm (AOA) are new meta-heuristic optimization algorithms proposed recently. SMA has strong ability of global exploration, but the oscillation effect is weak in the late iteration. It is easy to fall into local optimum, and the contraction mechanism is not strong, which leads to slow convergence speed. AOA algorithm uses multiplication and division operator to update position, which has strong randomness and good ability to avoid premature convergence. To solve the above problems, this paper combines the two algorithms and uses random opposition-based learning strategy to improve the convergence speed, and proposes a hybrid algorithm of slime mould algorithm and arithmetic optimization algorithm based on random opposition-based learning (HSMAAOA) with superior performance and high efficiency. The improved algorithm retains the SMA’s exploration phase and the exploitation phase will be replaced by the multiplication and division operators, which improves the capacity of the algorithm and the ability to jump out of the local optimal solution. In addition, random opposition-based learning strategy is used to enhance the diversity of the improved algorithm population and improve the convergence speed. The experimental results show that the HSMAAOA algorithm has good robustness and optimization accuracy, and significantly improves the convergence speed. Finally, the applicability and effectiveness of HSMAAOA in engineering problems are verified through the design of welded beams and the design of pressure vessels.