基于自适应扰动的疯狂蝴蝶算法

蝴蝶优化算法作为新提出的自然启发算法，其寻优方式模拟了蝴蝶利用嗅觉来确定花蜜或交配对象位置的行为。针对蝴蝶优化算法求解精度不高和收敛速度慢等问题，提出一种基于自适应扰动的疯狂蝴蝶算法（CIBOA）。首先，在自身认知飞行部分引入自适应惯性权重，平衡算法的局部与全局搜索能力；其次，在全局最优位置引入扰动策略，避免算法陷入局部最优；最后，在花蜜位置引入疯狂因子以增加种群多样性，获取更好的最优解。通过8个基准函数对5种算法搜索性能在10、30和50维的情况下进行对比分析，仿真实验结果表明改进算法的综合表现要优于其他算法。

Crazy butterfly algorithm based on adaptive perturbation

Butterfly optimization algorithm is a newly natural heuristic algorithm, which simulates the butterfly''s behavior of using smell to determine the location of nectar or mating object. To solve the problem that the butterfly optimization algorithm was prone to low accuracy and slow convergence, this paper proposed the crazy butterfly optimization algorithm integrating with adaptive perturbation(CIBOA). Firstly, it introduced the adaptive inertial weights to the self-cognitive flight part to balance the local and global search capabilities of the algorithm. Secondly, it introduced the perturbation strategy to the global optimal position to avoid the algorithm falling into the local optimal. Finally, it introduced the crazy operator to the nectar location to increase the population diversity and obtain better optimal solution. To compare the search performance of five algorithms in 10, 30 and 50 dimensions used eight benchmark functions. The experimental simulation results show that the comprehensive performance of the improved algorithm is better than other algorithms.