摘要
针对难以用数学模型表达的优化问题或带有随机性的优化问题,基于仿真的优化方法是其惟一的选择。因此,综述了基于仿真的优化方法的研究情况,介绍了仿真用于策略验证、基于仿真的Genetic Algorithm(GA)、基于仿真的Simulated Annealing(SA)、基于仿真的Particle Swarm Optimization(PSO)和仿真用于获取随机参数或函数等方法的实现。概括了基于仿真的优化方法在各领域的应用,并结合国内外的研究情况,对基于仿真的优化方法的难点进行了总结,并对其发展方向作了分析。
Simulation-based optimization （SBO）is one of the most promising researching fields, it is the best tool to solve the problems which possess stochastic property or can not be expressed explicitly by mathematical model. The development of SBO is generalized. Methods of testing strategies with simulation, simulation-based CA, simulation-based SA, simulation-based PSO and obtaining stochastic parameters and functions are introduced. Finally, the applications of SBO is summarized. The key points of SBO is analyzed and the development trend of SBO is predicted.
出处
《控制工程》
CSCD
2008年第6期672-677,702,共7页
Control Engineering of China
基金
国家自然科学基金重点资助项目(70431003)
创新群体基金资助项目(60521003)
国家科技支撑计划基金资助项目(2006BAH02A09)
