A Novel Variable Population Size Artificial Bee Colony Algorithm with Convergence Analysis for Optimal Parameter Tuning

Abstract

This paper introduces a novel algorithm called variable population size artificial bee colony (VPS-ABC) optimization algorithm. VPS-ABC is proposed to overcome the impact of the effect of initial population and improve the convergence rate of classical ABC. The main idea is based on reducing the number of food sources gradually and moving the bees towards the global best food source in each re-initialization process. Moreover, an analysis for convergence of the ABC algorithm is proofed in details. The convergence analysis is based on the relation between ABC variants and the general solution of the food source regeneration equation. To show the fitness of the proposed algorithm, a comparison is made between VPS-ABC versus classical ABC, PSO, and GA algorithms in tuning the proportional-integral-derivative (PID) controllers. Simulation results show that VPS-ABC algorithm is highly competitive, often outperforming PSO and GA algorithms.