Abstract
In this paper, we hybridize the grey wolf optimization (GWO) algorithm with the newly proposed slime mould algorithm (SMA). Comparisons had been made and three kinds of benchmark functions were introduced to verify the capability. 100 Monte Carlo simulation experiments had been carried on to reduce the influence of randomness as less as possible. Results showed that the performance of hybrid GWO-SMA would base on the given characteristics of problems themselves because of the random threshold parameter p and the multiple branches in the updating equation. The hybridization of the GWO and SMA might be not recommended for steady applications and engineering problems.
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