融合多策略改进的自适应乌鸦搜索算法

Adaptive Crow Search Algorithm with Multiple Strategy Improvements

针对乌鸦搜索算法存在收敛精度低,寻优速度慢,位置更新存在盲目性等缺陷,提出了一种融合多策略改进的自适应乌鸦搜索算法(Adaptive Crow Search Algorithm with Multiple Strategy Improvements,ACSA)。首先,通过引入一种记忆遗忘机制,不仅提高了算法的收敛速度和精度,而且能够保持种群的多样性。当个体乌鸦发现存在跟随者时,引入了黄金正弦算法进行位置更新,克服了位置更新存在盲目性的不足,从而提高了算法的收敛精度。同时改进了自适应感知概率和飞行步长,以此提高算法的寻优速度和精度。将本算法运用于13个基准测试函数和三杆桁架的设计问题,并同其他的算法进行试验对比,并将实验结果进行Wilcoxon秩和检验以及Friedman检验。实验结果表明,改进后的算法在函数优化以及三杆桁架的工程优化问题上,均能够较好地寻优求解,算法的求解精度和收敛速度均得到了一定的提升。

Aiming at the shortcomings of the crow search algorithm,such as low convergence accuracy,slow optimization speed,and blind location update,an Adaptive Crow Search Algorithm with Multiple Strategy Improvements(ACSA)was proposed.First,by introducing a memory and forgetting mechanism,not only the convergence speed and precision of the algorithm are improved,but also the diversity of the population can be maintained.When the individual crow finds that there are followers,the golden sine algorithm is introduced to update the position,which overcomes the lack of blindness in the position update,thus improving the convergence accuracy of the algorithm.At the same time,the adaptive perception probability and flight step are improved,so as to improve the optimization speed and accuracy of the algorithm.This algorithm is applied to 13 benchmark test functions and the design problem of three-bar truss,and compared with other algorithms,and the experimental results are carried out by Wilcoxon rank sum test and Friedman test.The experimental results show that the improved algorithm can better solve the problem of function optimization and engineering optimization of three-bar truss,and the algorithm's solution accuracy and convergence speed have been improved to a certain extent.