受强弱关系理论启发的改进人工蜂群算法

针对原人工蜂群算法在寻优过程中存在收敛精度不高、容易陷入局部最优的问题，提出一种改进人工蜂群算法(SWT-ABC)。将社会学中强弱关系模型化并引入多子群矩阵式蜂群结构，定义了强关系个体从三个方向随机引导搜索，加快算法收敛速度和提高收敛精度；为增强算法跳出局部最优的能力，定义了弱关系个体交互以实现子群间信息交流来提升种群多样性；增加侦查蜂反向学习机制并确定合适的蜜源上限，能有效提升目标函数评价次数的利用效率。通过基准测试函数的数值实验并与12种改进算法进行对比，改进后的人工蜂群算法收敛精度更高、全局寻优能力更强，并且在高维优化问题求解中仍具备良好的收敛性能。

Improved artificial bee colony algorithm based on strong and weak ties theory

To solve the problems of low convergence accuracy and easy to fall into local optimum in the optimization process of the original artificial bee colony algorithm, this paper proposed an enhanced artificial bee colony algorithm with strong and weak ties theory(SWT-ABC). Firstly, it defined the strong and weak ties model in a matrix multi-groups structure and stipulated that the strong ties individuals randomly guided the search process from three directions to improve the convergence speed and accuracy; In order to enhance the ability to jump out of the local optimum, SWT-ABC adopted the interaction of weak ties individuals among subgroups to enhance the population diversity; Finally the algorithm added the reverse learning mechanism of scout bees and determined appropriate upper limit of honey source to improve the utilization efficiency of the evaluation times. Through the numerical experiment of benchmark functions and comparisons with 12 improved intelligent optimization algorithms, it shows that the SWT-ABC has high convergence accuracy and good robustness, and also gets good performance in solving high-dimensional optimization problems.