Is the Bee Colony Optimisation algorithm suitable for continuous numerical optimisation? Online publication date: Tue, 03-Oct-2017
by Miloš Simić
International Journal of Metaheuristics (IJMHEUR), Vol. 6, No. 4, 2017
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.
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