Abstract
Another version of Artificial Bee Colony (ABC) optimization algorithm, which is called the Compact Artificial Bee Colony (cABC) optimization, for numerical optimization problems, is proposed in this paper. Its aim is to address to the computational requirements of the hardware devices with limited resources such as memory size or low price. A probabilistic representation random of the collection behavior of social bee colony is inspired to employ for this proposed algorithm, in which the replaced population with the probability vector updated based on single competition. These lead to the entire algorithm functioning applying a modest memory usage. The simulations compare both algorithms in terms of solution quality, speed and saving memory. The results show that cABC can solve the optimization despite a modest memory usage as good performance as original ABC (oABC) displays with its complex population-based algorithm. It is used the same as what is needed for storing space with six solutions.
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Dao, TK., Chu, SC., Nguyen, TT., Shieh, CS., Horng, MF. (2014). Compact Artificial Bee Colony. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_11
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DOI: https://doi.org/10.1007/978-3-319-07455-9_11
Publisher Name: Springer, Cham
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