A food source-updating information-guided artificial bee colony algorithm
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Artificial bee colony algorithm simulates the foraging behavior of honey bees, which has shown good performance in many application problems and large-scale optimization problems. To model the bees foraging behavior more accurately, a food source-updating information-guided artificial bee colony algorithm is proposed in this paper. In this algorithm, some food source-updating information obtained during optimizing time is introduced to redefine the foraging strategies of artificial bees. The proposed algorithm has been tested on a set of test functions with dimension 30, 100, 1000 and compared with some recently proposed related algorithms. The experimental results show that the performance of artificial bee colony algorithm is significantly improved for both rotated problems and large-scale problems. Compared with the related algorithms, the proposed algorithm can achieve better or competitive performance on most test functions and greatly better performance on parts of test functions.
KeywordsForaging strategies Running information Artificial bee colony Single-objective optimization
This work was supported by the National Natural Science Foundation Program of China (61572116, 61572117, 61502089), the National key Techonlogy R&D Program of the Ministry of Science and Technology (2015BAH09F02), the Provincial Scientific and Technological Project (2015302002), and the Special Fund for Fundamental Research of Central Universities of Northeastern University (N150408001, N150404009).
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