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A grey artificial bee colony algorithm
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DOI：10.1016/j.asoc.2017.06.015.png)
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Artificial bee colony (ABC) algorithm is a very popular population-based algorithm. Unfortunately, there exists a shortcoming of slow convergence rate, which partly results from random choices of neighbor individuals regarding its solution search equation. A novel scheme for the choice of neighbors is introduced based on grey relational degrees between a current individual and its neighbors to overcome the insufficiency. Then, the chosen neighbor is used to guide the search process. Additionally, inspired by differential evolution, a solution search equation called ABC/rand/2 is employed to balance the previous exploitation and a new perturbation scheme is also employed. What is more, solution search equations using information of the best individual, an opposition-based learning method and a chaotic initialization technique are also integrated into the proposed algorithm called grey artificial bee colony algorithm (GABC for short). Subsequently, the effectiveness and efficiency of GABC are validated on a test suite composed of fifty-seven benchmark functions. Furthermore, it is also compared with a few state-of-the-artalgorithms. The related experimental results show the effectiveness and superiority of GABC. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Artificial bee colony algorithm
Grey relational analysis
Opposition based learning
Chaotic initialization
Numerical optimization
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6.6
论文数:
1.4W
被引数:
4.8W
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