An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation

Abstract

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.