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Enhancing firefly algorithm with adaptive multi-group mechanism
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DOI：10.1007/s10489-021-02766-9.png)
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Abstract
En 中文
Firefly algorithm (FA) is efficient in solving continuous optimal problems, because of its ability to a global search. However, the redundant attractions and incorrect directions may reduce the efficiency of FA. To improve the performance of FA, a novel multi-group mechanism is proposed based on an assumption that firefly has a visual field. The modified firefly algorithm is called the visual firefly algorithm(VFA). The framework of VFA combines the assumption with the designed strategies to balance the exploration and exploitation. Where the proposed observer strategy works for the exploration, the suggested selective random strategy plays the role of the exploiter. To verify the performance of the presented algorithm, extensive experiments are executed on CEC2013 benchmark functions. Additionally, the efficiency of the proposed multi-group mechanism is analyzed in-depth. The experimental results reveal that the proposed multi-group mechanism improves FA and provides a suitable solution for most CEC2013 problems with different dimensions. Especially, its performance remains robust, where the problems become more complex.
Keywords:
Optimization
Firefly algorithm
Multi-group
Efficiency
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