Self-adaptive artificial bee colony for function optimization

Artificial bee colony (abc) is a novel population-based optimization method,having the advantage of less control parameters,being easy to implement,and having strong global optimization ability. however,abc algorithm has some shortcomings concerning its position-updated equation,which is skilled in global search and bad at local search. in order to coordinate the ability of global and local search,we first propose a self-adaptive abc algorithm (denoted as sabc) in which an improved position-updated equation is used to guide the search of new candidate individuals. in addition,good-point-set approach is introduced to produce the initial population and scout bees. the proposed sabc is tested on 12 well-known problems. the simulation results demonstrate that the proposed sabc algorithm has better search ability with other several abc variants. © 2017 mingzhu tang et al.