Opposition-based learning equilibrium optimizer with Levy flight and evolutionary population dynamics for high-dimensional global optimization problems

The equilibrium optimizer (EO) is a recently proposed physics-based metaheuristic algorithm inspired by the dynamic mass balance on a control volume. However, EO may encounter local convergence and low accuracy when solving high-dimensional optimization problems. In this work, we propose the opposition-based learning EO algorithm with the Levy flight and the evolutionary population dynamics for high-dimensional global opti-mization problems, called EOOBLE. Firstly, the opposition-based learning strategy is embedded into the initialization and updating process of EO. Then, the Levy flight strategy is used to improve the exploration ability of EO in the updating mechanism. Moreover, the strategy of evolutionary population dynamics is used in the proposed algorithm to avoid falling into the local optimum. The performance of the proposed algorithm is tested by 25 benchmark functions with dimensions from 100 to 5000, and is compared with 8 state-of-the-art meta -heuristic algorithms. The statistical results indicate that the proposed algorithm has better convergence capacity than the compared algorithms. Besides, the proposed algorithm is also compared with different variants of EO, which outperforms the original EO and variants of EO with one or two operators. Therefore, the EOOBLE is a competitive algorithm in solving high-dimensional global optimization problems. Finally, a high-dimensional engineering design problem demonstrates the effectiveness of the EOOBLE.