Authors: Miloš Simić
Addresses: Marka Simovića 17 B, 34000 Kragujevac, Serbia
Abstract: Bee colony optimisation (BCO) is a nature-inspired swarm metaheuristic for solving hard optimisation problems. It has successfully been applied to various areas of science, industry, and engineering. However, all those cases belong to the field of combinatorial optimisation. This paper is among the first to test BCO's capacities for solving continuous numerical optimisation problems. We found that the performance of the algorithm depended on the settings of its parameters and characteristics of the optimisation problems to which it was applied. We examined for which types of numerical functions our implementation of improvement-based BCO, known as BCOi, performed well and which classes it was not able to handle successfully. Also, following the design of experiments (DoE) approach, we analysed how the parameters of the algorithm affected its performance and provided some useful explanations that might hold for other applications of our version and other variants of BCO.
Keywords: bees foraging principles; nature-inspired metaheuristics; numerical optimisation; optimisation problems; swarm intelligence.
International Journal of Metaheuristics, 2017 Vol.6 No.4, pp.279 - 308
Received: 22 Jun 2016
Accepted: 09 Mar 2017
Published online: 02 Aug 2017 *