精英正交学习萤火虫算法

针对萤火虫算法后期收敛较慢以及求解精度不高的问题,提出了精英正交学习萤火虫算法。该算法利用精英萤火虫采用正交学习策略来构造指导向量,以保存和发现最优方向信息,从而引导群体更准确地飞向全局最优区域。同时,还采用了自适应步长技术来更好地平衡算法探索与开发能力,采用最小吸引力参数保证高维空间距离过大的个体之间的相互吸引。在6个经典测试函数上与标准萤火虫算法及其它3种改进的萤火虫算法进行了对比,实验结果表明,提出的算法具有较快的收敛速度和较高的收敛精度。

Elite Orthogonal Learning Firefly Algorithm

In order to overcome the shortcomings of firefly algorithm such as slow convergence speed and low computational accuracy,an elite orthogonal learning firefly algorithm was proposed.An elite firefly was introduced to construct a guidance vector using the orthogonal learning strategy,which can preserve and discover useful information in the population best positions and direct the swarm to fly toward the global optimal region.At the same time,the method of adaptive step size was used to balance the exploration and exploitation ability of the algorithm,and the minimum attractive parameter was adopted to guarantee the attraction among the fireflies whose distance is large.We compared the proposed algorithm with standard firefly algorithm and other three improved firefly algorithms on six benchmarks,and the results show that the proposed algorithm obtains quicker convergence speed and better solution accuracy.