遗传算法(Genetic Algorithm，简称GA)是20世纪70年代由Holland提出的一种模仿生物进化过程的有效的优化方法，能根据已有的知识积累，按照概率寻优机制进行全局搜索未知空间，而且还可以根据问题的不同进行变通，所以得到了广泛的应用。但是，传统遗传算法存在着早熟收敛和收敛速度慢的缺点，这是由算法的两个主要因素“选择压力”和“种群多样性”造成的。所以国内外的学者们通过各种改进研究来平衡它们之间的矛盾，虽然各种改进的遗传算法取得了一定的成就，但多是局限在某一个方面，没有从总体上进行把握。而且，它们偏重于应用，缺乏收敛性和有效性方面的理论证明。 本文针对传统遗传算法存在“种群多样性”和“选择压力”之间矛盾的问题，提出一种基于自然界蜂群繁殖原理的改进遗传算法－蜂群遗传算法(Bee-Swarm Genetic Algorithm，简称BSGA)。蜂群是由蜂后、雄蜂和工蜂组成的，BSGA主要操作算子包括蜂后和雄蜂的绝对交配权，蜂后和工蜂的相似性抑制，蜂后的模拟退火局部寻优机制，雄蜂和蜂后的自适应交叉算子以及工蜂的自适应变异算子。其次，通过对马尔科夫链模型和遗传机制的分析，证明了蜂群遗传算法的收敛性和有效性。最后通过对几个经典函数的优化以及 皇后问题( 难)的组合优化实验验证了该算法具有较好的搜索性能和较少的计算量。

Genetic Algorithm was proposed in 1970s by professor Holland. It is an effective optimization method to stimulate the evolution of nature. Based on the accumulated knowledge, GA can not only globally search the unknown space by probability searching mechanism, but also change by the different problem, so it is widely applied. However, the traditional GA has the disadvantage of dealing with the slower rate and premature convergence, causing by the contradiction between population diversity and pressure of selection. So many different methods are proposed to balance their contradiction. Although a lot of improved GAs make progress, it still has some limitations. Bee-Swarm genetic algorithm based on reproducing of swarm is proposed in this thesis, to solve the contradiction between population diversity and pressure of selection. The swarm is composed of queen bee, drones and worker bees, the main operators of the algorithm include the absolute mating right between queen bee and drones, the simulated suppression between queen bee and worker bees, local optimization of queen bee based on simulated annealing, adaptive crossover between drones and queen bee, adaptive mutation of worker bee. Then the convergence and validity are proved by the analysis of Markov mode and genetic mechanism. Finally, the simulation results about the optimization of functions and combinational optimization about N-queen problem show that it has both validity and less computational magnitude.