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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Compact Bat Algorithm
verfasst von : Thi-Kien Dao, Jeng-Shyang Pan, Trong-The Nguyen, Shu-Chuan Chu, Chin-Shiuh Shieh
Erschienen in: Intelligent Data analysis and its Applications, Volume II
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Addressing to the computational requirements of the hardware devices with limited resources such as memory size or low price is critical issues. This paper, a novel algorithm, namely compact Bat Algorithm (cBA), for solving the numerical optimization problems is proposed based on the framework of the original Bat algorithm (oBA). A probabilistic representation random of the Bat’s behavior 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 cBA can solve the optimization despite a modest memory usage as good performance as oBA displays with its complex population-based algorithm. It is used the same as what is needed for storing space with six solutions.