求解仿真优化问题的知识型启发式搜索方法

仿真优化研究基于仿真的目标优化问题,已经成为系统仿真和运筹学等领域共同关注的热点和前沿课题.针对离散事件动态系统仿真优化中的难点问题,提出了一种全新的知识型启发式搜索方法.采用知识模型和启发式搜索模型相结合的集成建模思路,以启发式搜索模型为基础,同时突出知识模型的作用,将启发式搜索模型和知识模型进行优化组合、优势互补,以提高启发式搜索技术的效率.基于期望值模型的数值仿真,验证了方法的可行性和有效性.仿真结果表明,无论是求解质量还是求解速度,都优于其它几种现有方法.研究结果表明,将知识模型合理地嵌入到现有启发式搜索方法中,可以有效地解决复杂的仿真优化问题.

A Knowledge-Oriented Heuristic Algorithm for Solving Simulation Optimization Problems

Simulation optimization is a hot and new topic in the fields of system simulation,operational research and so on.In view of the hardness of the simulation optimization of discrete event dynamical systems,the paper presents a novel knowledge-oriented heuristic algorithm.This method integrates the knowledge model with the heuristic searching model.The heuristic searching model plays an essential role and the knowledge model plays an important assistant role in this technique.Through appropriate combination and indispensable supplement,these two models can improve the optimization effectiveness of the proposed approach.Numerical simulation based on work shop production problem demonstrates the feasibility and effectiveness of the proposed approach.The experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.The research results of this paper suggest that integrating the knowledge model with the heuristic searching model is an efficacious approach for the complex simulation optimization problems.