关键词: 群智能优化算法, 蝙蝠算法（BA）, 图像分割, 最小交叉熵

Abstract:

The traditional Minimum Cross Entropy Threshold segmentation method（MCET） uses an exhaustive search form, which has the disadvantages of large computational complexity and low segmentation efficiency, which largely limits its application. Aiming at the shortcomings of the minimum cross entropy segmentation method, an improved Bat Algorithm（BA） is proposed to search the optimal solution of the threshold. Making adaptive adjustments to the weight parameters in the BA algorithm, the inertial weighting strategy that changes with the number of iterations is applied to the BA algorithm update formula, and three different improved strategies are given to solve the problem of the decline of the convergence speed of the original BA algorithm as it approaches the optimal solution. The Improved optimal BA algorithm（IBA） is applied to the minimum cross-entropy multi-threshold image segmentation. In order to explore the performance of the segmentation algorithm, it is compared with the basic BA algorithm, the Improved Particle Swarm Optimization algorithm（IPSO）, and the Fuzzy Clustering method（FC）. Experimental results show that the proposed IBA algorithm is significantly better than other algorithms in terms of operation speed and segmentation accuracy.

Key words: swarm intelligence optimization algorithm, Bat Algorithm（BA）, multi-threshold image segmentation, minimum cross entropy