کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
478455 | 1446086 | 2012 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Retrospective optimization of mixed-integer stochastic systems using dynamic simplex linear interpolation Retrospective optimization of mixed-integer stochastic systems using dynamic simplex linear interpolation](/preview/png/478455.png)
We propose a family of retrospective optimization (RO) algorithms for optimizing stochastic systems with both integer and continuous decision variables. The algorithms are continuous search procedures embedded in a RO framework using dynamic simplex interpolation (RODSI). By decreasing dimensions (corresponding to the continuous variables) of simplex, the retrospective solutions become closer to an optimizer of the objective function. We present convergence results of RODSI algorithms for stochastic “convex” systems. Numerical results show that a simple implementation of RODSI algorithms significantly outperforms some random search algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
► Formulation of mixed integer stochastic optimization (MISO) problems.
► Retrospective optimization using dynamic simplex interpolation (RODSI).
► Global convergence of RODSI algorithms for stochastic “convex” systems.
► Numerical analysis of RODSI performance.
Journal: European Journal of Operational Research - Volume 217, Issue 1, 16 February 2012, Pages 141–148