Binary artificial bee colony optimization

Abstract

Artificial bee colony (ABC) optimization is a relatively new population-based, stochastic optimization technique. ABC was developed to optimize unconstrained problems within continuous-valued domains. This paper proposes three versions of ABC that enable it to be applied to optimization problems with binary-valued domains. The performances of these binary ABC algorithms are compared on a benchmark of unconstrained optimization problems. The best of these algorithms, i.e. angle-modulated ABC (AMABC), is then compared with the angle-modulated particle swarm optimizer and the angle-modulated differential evolution algorithm.