关键词: 智能优化算法, 生物地理学优化算法, 图像分割, 多阈值分割, 最大类间方差法

Abstract: In view of more optimization difficulty with higher dimensions in the high-dimensional multilevel thresholding and the low optimization efficiency of biogeography-based optimization (BBO) algorithm, this paper proposes an efficient BBO (EBBO) algorithm applied to high-dimensional OTSU multilevel image thresholding. Firstly, a novel little disturbance mutation operator is taken, and the operator is blended into the heuristic operator to get a new migration operator, which simplifies the processes of the proposed algorithm, such as not computing mutation probability or setting its parameters, in order to improve the efficiency. Secondly, the roulette wheel selection is     replaced with the example learning approach and the immigration rate is calculated once before the iteration loop   instead of calculating many times in the original algorithm to reduce the computational complexity further. Thirdly, a greedy selection operator is used instead of the original elitist selection operator to accelerate the convergence process. Finally, the EBBO algorithm is applied to high-dimensional OTSU multilevel image thresholding. The experimental results show that the performance of proposed EBBO is better, faster and more stable than standard BBO, EBO, BDE and MKTO, and there are fewer parameters to be tuned in the EBBO.

Key words: intelligent optimization algorithm, biogeography-based optimization algorithm, image segmentation, multilevel thresholding, maximal between-cluster variance