求解约束优化问题的文化算法研究

Research on Cultural Algorithm for Solving Nonlinear Constrained Optimization

文化算法的主要思想是明确地从进化种群中获得求解问题的知识(即信念)并用于指导搜索过程.本文提出了一种基于多层信念空间的文化算法,该算法通过对多层信念空间的择优选用将提取的知识用于提高进化计算性能来解决约束优化问题.应用实例表明该算法具有较好的结果和较少的计算量.

The key idea behind cultural algorithm （CA） is to explicitly acquire problem-solving knowledge （beliefs） from the evolving population and in return apply that knowledge to guide the search. In this paper, we propose a CA based on multilayer belief spaces that selects the best belief space from the multilayer belief spaces so as to apply the extracted knowledge to improve the performance of evolutionary algorithm used for constrained optimization. Examples show that the algorithm produces highly competitive results at a relatively low computational cost.