ABSTRACT Butterfly Optimisation Algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy are applied to the original algorithm. By adding guiding weight to the global search equation, the convergence speed and accuracy of the algorithm are improved, and the possibility of jumping out of the local optimal solution is increased by the population restart strategy. In order to verify the performance of the proposed algorithm, 24 benchmark functions commonly used for optimisation algorithm experiments are applied in this paper, including 12 unimodal functions and 12 multimodal functions. Experimental results show that the proposed algorithm improves the convergence speed, accuracy and the ability to jump out of the local optimal solution.

中文翻译：

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基于引导权重和种群重启的改进蝴蝶优化算法

摘要 蝴蝶优化算法（BOA）是一种基于蝴蝶觅食策略的元启发式群智能算法，但在求解高维优化问题时，在收敛速度和精度方面仍有待提高。本文提出了一种改进的蝴蝶优化算法，将引导权重和种群重启策略应用于原算法。通过在全局搜索方程中加入引导权重，提高了算法的收敛速度和精度，并增加了种群重启策略跳出局部最优解的可能性。为了验证所提算法的性能，本文应用了优化算法实验中常用的24个基准函数，包括12个单峰函数和12个多峰函数。实验结果表明，该算法提高了收敛速度、精度和跳出局部最优解的能力。