基于种群多样性的自适应乌鸦搜索算法

针对原始乌鸦搜索算法对种群多样性控制不强、个体位置更新方式单一、局部搜索精细度不高等缺点，提出新的自适应乌鸦搜索算法.设计多种搜索引导个体，基于进化不同阶段的种群多样性，实现搜索引导个体的自适应选择策略，使算法在迭代前期加强全局勘探，在迭代后期强化局部开发.结合正余弦搜索理念，构建基于线性递减、混合正余弦震荡递减的多种飞行长度控制参数及相应的多种搜索方式，提升算法的搜索遍历性，增加算法在迭代后期找到更优解的概率.为了验证新算法的有效性，通过标准测试函数，将新算法与原始乌鸦搜索算法、改进乌鸦搜索算法和其他优秀的智能优化算法进行仿真实验，比较分析各算法的收敛精度、收敛速度、稳定性、Wilcoxon符号秩检验和Friedman检验.实验结果表明，新算法的性能优于其他比较算法的性能，新算法实现了全局勘探和局部开发、收敛精度和收敛速度的平衡.

Adaptive crow search algorithm based on population diversity

A new adaptive crow search algorithm was proposed to solve the shortcomings of the original crow search algorithm, such as weak control of population diversity, single updating of individual position and low precision of local search. Firstly, multiple search-guided individuals were designed, and an adaptive selection strategy of search-guided individuals was realized based on population diversity at different stages of evolution. The global exploration in the early iteration and local exploitation in the late iteration were achieved using the strategy. Secondly, by combining the idea of sine-cosine search, several flight length control parameters based on linear decline or mixed sine-cosine oscillation decline were used to constructed different search modes for improving the search ergodicity of the algorithm and increasing the probability that the algorithm finding a better solution in the late iteration. Thirdly, to verify the effectiveness of the new algorithm, standard test functions were selected, and the new algorithm was simulated with the original crow search algorithm, the improved crow search algorithms, and other excellent intelligent optimization algorithms. All the algorithms were compared and analyzed in terms of convergence accuracy, convergence speed, stability, Wilcoxon signed rank and Friedman tests. Experimental results show that the performance of the new algorithm is better than that of other comparison algorithms, and the balance between global exploration and local exploitation, convergence accuracy and convergence speed are achieved by the new algorithm.