Multi-strategy optimization of HHO algorithm for path planning of warehouse robots

Abstract

Aimed at path planning of warehouse robots in a storage environment, this paper proposes a multi-strategy Harris Hawks Optimization algorithm (HHO). To better deal with the uneven population distribution caused by the high randomness of individual initialization, an escape energy updating mechanism was proposed to improve population information exchange. Considering that the global optimal individual was not chosen as the guide to update location in the searching process, a searching mechanism is designed based on the maximum and minimum of the individual location, which effectively improves the tightness in the searching process. The adaptive elastic factor is introduced to disturb the current local individual and realize the rapid escape from the local optimal. After the tests of 23 benchmark functions, the multi-strategy HHO algorithm is proved to be superior to HHO in convergence speed and accuracy. Lastly, the proposed HHO algorithm is applied to three different scale warehouse grid environments, which further verifies its applicability to the high-dimensional path planning problem, and effectively improves warehouse operation efficiency.