Research on the searching performance of flower pollination algorithm with three random walks

Abstract

Flower pollination algorithm is a new type of heuristic algorithm, which uses Lévy random walk as the key element for high efficiency of global searching. In order to explore the search performance of pollination algorithms under different random walk models, three random walk models are taken into account, including levy random walk model used by original flower pollination algorithm and two new random walk models based on McCulloch algorithm introduced in this paper. The analysis of searching performance and adaptive of Flower Pollination Algorithm with three random walks from two aspects of the model structure and the numerical simulation is given. The result shows that Cauchy random has a great competitive advantage for the low dimensional searching problem, and the Gauss random is more suitable for dealing with the multi-dimension unimodal case, while the Lévy random is able to provide better performance of solving the multi-dimension multimodal case. Simulation results and analysis will have a significant impact on the design of the randomness mechanism of the meta-heuristics algorithm and the improvement of Classical algorithms.