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A multi-group firefly algorithm for numerical optimization

机译:用于数值优化的多组Firefly算法

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摘要

To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). A Firefly colony is divided into several subgroups with different model parameters. Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. And then, each firefly achieves local search by following the brighter firefly in its neighbors. At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. Experimental results verify the effectiveness of the proposed algorithm.
机译:为解决萤火虫算法(FA)过早收敛的问题,本文分析了算法的演化机制,并提出了一种改进的基于修改演化模型和多组学习机制的萤火虫算法(IMGFA)。萤火虫殖民地分为几个具有不同模型参数的子组。在每个子组内,最佳萤火虫负责领导其他萤火虫以实施早期全球演变,并在萤火虫中建立信息相互系统。然后,每个萤火虫在其邻居中遵循更明亮的萤火虫实现本地搜索。与此同时,各个子组最好的萤火虫中的学习机制可以帮助人口更有效地获得全球优化目标。实验结果验证了所提出的算法的有效性。

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