Self-Adaptive Artificial Bee Colony for Function Optimization

Abstract EN

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.