A hybrid grasshopper optimization algorithm with bat algorithm for global optimization

This paper introduces a hybrid grasshopper optimization algorithm with bat algorithm (BGOA) for global optimization. In the BGOA, the Levy flight with variable coefficient is employed to enhance the exploration capability of the GOA. Then, the local search operation of bat algorithm (BA) is combined to balance the exploration and exploitation. Additionally, the random strategy is introduced and applied to high quality population to improve the exploitation capability in the searching process. The performance of BGOA is evaluated on 23 benchmark test functions, and compares with genetic algorithm (GA), bat algorithm (BA), moth-flame optimization algorithm (MFO), dragonfly algorithm (DA) and basic GOA. The results establish that the BGOA is able to provide better outcomes than the other algorithms.

Automated summary

This paper introduces a hybrid grasshopper optimization algorithm with bat algorithm (BGOA) for global optimization, which combines Levy flight, bat algorithm's local search operation, and a random strategy to enhance exploration and exploitation. The performance evaluation on 23 benchmark test functions demonstrates that BGOA outperforms other algorithms.