增强性人工蜂群算法及在多阀值图像分割中的应用

摘要

A modified artificial bee colony optimizer (MABC) is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main idea of MABC is to enrich artificial bee foraging behaviors by combining local search and comprehensive learning using multi-dimensional PSO-based equation. With comprehensive learning, the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the local search enables the bees deeply exploit around the promising area, which provides a proper balance between exploration and exploitation. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the MABC algorithm to image segmentation problem. Experimental results verify the effectiveness of the proposed algorithm.%提出了一种改进的人工蜂群算法来处理图像分割问题,具体采用一系列群体优化觅食策略来平衡开发和探测寻优模式.该算法的主要思想是将局部搜索策略和基于多维粒子群方程的复杂学习策略相结合,可丰富人工蜂群觅食行为模式.通过全局学习,蜂群把全局最优信息整合到搜索方程中以提高探测搜索能力,同时局部搜索使蜂群能更深层探索优势区域,最终取得开发和探索平衡.通过比较该改进蜂群算法和进化算法、群智能算法在一系列基准函数上的实验结果,表明本文所提出的算法的有效性.将改进蜂群算法应用于处理图像分割问题,实验结果也证明了该算法的有效性