遗传-人工蜂群融合算法及其Markov收敛性分析
COMBINATION ALGORITHM OF GENETIC-ARTIFLCIAL BEE COLONY AND ITS MARKOV CONVERGENCE ANALYSIS

本文研究了遗传算法易发生“早熟”以及人工蜂群算法在搜索初期寻优速度慢的问题.基于将遗传算法与人工蜂群算法融合以实现二者互补的思想，提出遗传-人工蜂群融合算法（G-ABCA），利用马尔可夫理论对其收敛性进行了理论分析，证明其适应度函数值序列（即优化解满意值序列）是单调且收敛的，并利用四个经典的多峰测试函数对遗传-人工蜂群融合算法、改进的遗传算法以及人工蜂群算法进行了对比实验分析，结果表明：遗传-人工蜂群融合算法不仅收敛，而且其寻优性能显著优于其它两种算法.
In this paper, we study the problems that genetic algorithm's prematurity and artificial bee colony algorithm is slow at the beginning of the search. Based on the idea that combining genetic algorithm and artificial bee colony algorithm to achieve complementary, this paper proposes Genetic-Artificial Bee Colony Algorithm (G-ABCA), and analyses the convergence of G-ABCA by Markov theory. And it proves that the sequence of fitness function, which is also called the solution sequence, is monotonous and convergent. Meanwhile, it carries out the contrasting experiment and analysis of G-ABCA, improves genetic algorithm and artificial bee colony algorithm based on four classical multi-modal test functions, the results show that not only G-ABCA is convergent, but also its optimal capability is better than other two algorithms