一般克隆选择算法的收敛性证明*

克隆选择算法已经广泛应用于计算智能领域，而针对克隆选择算法理论方面的分析和研究工作却很少。为了丰富克隆选择算法的理论基础，采用了与研究遗传算法相似的方法，研究了克隆选择算法的收敛性，推导出克隆选择算法在求解优化问题时，收敛到全局最优解的充分条件。因此，对基于克隆选择算法的各种应用如BCA和CLONALG算法，只要检查这些充分条件是否满足就可以证明算法的收敛性。

Convergence proof for generic clonal selection algorithm

Clonal selection algorithms have been applied widely in intelligent computation field, but theoretical analysis and research works regarding clonal selection algorithm were lacking relatively. In order to enrich the theoretical underpinning of clonal selection algorithms, this paper investigated the convergence of generic clonal selection algorithms in a similar manner as performed previously for genetic algorithms. The sufficient conditions that generic clonal selection algorithms converge to global optimum for solving optimization problem were derived. Consequently, all kinds of applications based on clonal selection algorithm such as BCA and CLONALG algorithms, their convergence can be proved by examining whether the sufficient conditions are satisfied or not.