Scientific Publications

• Simić, M. (2017) "Is the bee colony optimisation algorithm suitable for continuous numerical optimisation?", Int. J. of Metaheuristics, Vol. 6, No. 4, pp.279--308.
• The accepted version of the article can be downloaded from here. The BibTex entry can be found here.
• 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. 

• Simić, M. (2018) "How to Estimate the Ability of a Metaheuristic Algorithm to Guide Heuristics During Optimization"
• The version submitted for peer review can be downloaded from here.
• It is also available at arXiv.
• Abstract: Metaheuristics are general methods that guide application of concrete heuristic(s) to problems that are too hard to solve using exact algorithms. However, even though a growing body of literature has been devoted to their statistical evaluation, the approaches proposed so far are able to assess only coupled effects of metaheuristics and heuristics. They do not reveal us anything about how efficient the examined metaheuristic is at guiding its subordinate heuristic(s), nor do they provide us information about how much the heuristic component of the combined algorithm contributes to the overall performance. In this paper, we propose a simple yet effective methodology of doing so by deriving a naive, placebo metaheuristic from the one being studied and comparing the distributions of chosen performance metrics for the two methods. We propose three measures of difference between the two distributions. Those measures, which we call BER values (benefit, equivalence, risk) are based on a preselected threshold of practical significance which represents the minimal difference between two performance scores required for them to be considered practically different. We illustrate usefulness of our methodology on the example of Simulated Annealing, Boolean Satisfiability Problem, and the Flip heuristic.