An Artificial Bee Colony Optimization Algorithm Based on Multi-exchange Neighborhood

Abstract

The artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent foraging behavior of honeybees. ABC algorithm gets the new solution by searching the neighborhood of the current solution in the search process and the scope searched is small, which leads to slow convergence and easily gets stuck to the local optimal solution. In this paper, an improved ABC algorithm is proposed based on multi-exchange neighborhood (MNABC) by exchanging neighborhood in the search process. The simulation experiment comparing MNABC with the basic ABC and PSO algorithms, shows that the proposed method can improve the convergence speed and global searching capability of ABC algorithm.