Self-adaptive position update in artificial bee colony

摘要: Artificial bee colony (ABC), which is one of the leading swarm intelligence based algorithm, dominates other optimization algorithms in some fields but, it also has drawbacks like premature convergence and slow later stages due to unbalanced exploration exploitation abilities. In this paper, we propose a novel variant ABC, namely Self-adaptive Position update ABC (SPABC), three position strategies are incorporated employed phase on fitness solutions. Each checks its accordingly adopts standard Gbest guided (GABC), modified (MABC). way, with set solution different characteristics can improve quality newly generated solutions hence speed ABC. During generations, suitable strategy self-adapted according solution. The performance SPABC reported 15 real parameter benchmark test problems compared recent variants, BSFABC, GABC, MABC.