基于信息熵的改进人工蜂群算法

为了克服人工蜂群算法在处理复杂性问题时收敛速度慢、收敛精度不高、易早熟等缺陷，在原始人工蜂群算法的基础上引入信息熵。信息熵本身是不确定性的一种度量，由信息熵的值来度量人工蜂群算法中跟随蜂选择的不确定性，通过控制信息熵的值达到控制算法中跟随蜂选择过程的目的，实现算法的自适应调节。通过对测试函数和不同规模TSP问题的模拟仿真，对人工蜂群算法、蚁群算法和其他改进方法进行了对比，验证了所提出改进方法的可行性和有效性。

Improved artificial bee colony algorithm based on information entropy

In order to overcome the defects of the artificial bee colony algorithm in convergence speed, convergence precision and early maturity when dealing with complexity problems, the information entropy is introduced into the artificial bee colony algorithm. The information entropy is a measure of uncertainty, the uncertain choice of onlookers in artificial bee colony algorithm is shown by information entropy value, and the choice of onlookers is controlled by information entropy value, which it realizes self-adaptive adjustment of artificial bee colony algorithms. Through simulating the test functions and TSP problems, comparing with the artificial bee colony algorithm, the ant colony algorithm and other improved algorithms, the feasibility and effectiveness of the method proposed are shown.