Evaluating Performance of Honey Bee Mating Optimization

Somayeh Karimi, Navid Mostoufi () and Rahmat Sotudeh-Gharebagh
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Somayeh Karimi: University of Tehran
Navid Mostoufi: University of Tehran
Rahmat Sotudeh-Gharebagh: University of Tehran

Journal of Optimization Theory and Applications, 2014, vol. 160, issue 3, No 17, 1020-1026

Abstract: Abstract The honey bee mating optimization (HBMO) algorithm is presented and tested with various test functions, and its performance is compared with the genetic algorithm (GA). It is shown that the HBMO algorithm can overcome the weaknesses of the GA. The HBMO converges faster than the GA. Even when the HMBO starts from a more improper initial condition than the GA, it can reach a better solution in a smaller number of function evaluations. Furthermore, in some cases, the GA was not able to reach the global minimum.

Keywords: Honey bees mating optimization; Swarm-based algorithms; Swarm intelligence (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10957-013-0336-2

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