The continuous catalytic regeneration (CCR) reforming process optimisation leads to nonlinear programming with nonlinear quality constraints such as octane number and coke concentration on the catalytic particles. A typical CCR reforming process consists of four reactors with recycle. The reaction patterns and reactors have been mathematically modelled on a base of 12-lumped kinetics reaction network derived from literature. The bee colony optimisation (BCO) algorithm is one of the most recent and efficient swarm intelligence-based algorithms which simulates the foraging behaviour of honey bee colonies. The performance of the BCO algorithm in the process optimisation was compared with the genetic algorithm (GA). In the present work, BCO algorithm was used for optimising the CCR reforming process. The results show that the BCO algorithm reaches a better optimum point in a lower evaluation time and higher convergence rate with respect to the GA. (c) 2012 Canadian Society for Chemical Engineering