随机进化搜索算法及其收敛性

Random Evolutionary Searching Algorithm and Its Convergence

针对工程中具有约束条件的非线性函数的全局优化问题,提出了一种基于生物进化思想的随机进化搜索优化算法,在多方面弥补了遗传算法的不足,既具有遗传算法的全局优化能力,又显著地减小了计算复杂度。通过理论推求,结果证明了随机进化搜索算法的收敛性,同时指出单变量与多变量随机进化搜索算法无本质上差异,仅是选取的概率密度函数不同,该算法行之有效。

This paper mainly proposes a random evolutionary searching algorithm of optimization especially for the global optimization problem of nonlinear function with constraints. This method is based on the evolutionary algorithm in engineering. The genetic algorithm is one kind of evolutionary algorithm, however, comparing to the genetic method, the random evolutionary searching algorithm has made some improvements especially for eliminating the drawbacks of the genetic algorithm. This method makes it not only have the ability of global optimization, but also decrease the complexity of computation greatly. Furthermore. the research has strongly proved the convergence of random evolutionary searching algorithm according to the theoretical reasoning. Simultaneously this paper points out that there is no difference between the single variable and multivariable based random evolutionary searching algorithm in nature, the only difference is the probability density function.