Artificial bee colony algorithm variants on constrained optimization

Abstract

Optimization problems are generally classified into two maingroups: uncon-strained and constrained. In the case of constrained optimization, specialtechniques are required to handle with constraints and to produce solutionsin the feasible space. Intelligent optimization techniquesthat do not makeassumptions on the problem characteristics are preferred to produce accept-able solutions to the constrained optimization problems. In this study, theperformance analysis of artificial bee colony algorithm (ABC), one of the intel-ligent optimization techniques, is examined on constrained problems and theeffect of some modifications on the performance of the algorithm is examined.Different variants of the algorithm were proposed and compared in terms ofefficiency and stability. Depending on the results, when DE operators were in-tegrated into ABC algorithm, an enhancement in the performance was gainedin addition to preserving the stability of the basic ABC. The ABC algorithmis a simple optimization algorithm that can be efficiently used for constrainedoptimization without requiring a priori knowledge.