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Retrospective optimization of mixed-integer stochastic systems using dynamic simplex linear interpolation

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  • Wang, Honggang

Abstract

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).

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  • Wang, Honggang, 2012. "Retrospective optimization of mixed-integer stochastic systems using dynamic simplex linear interpolation," European Journal of Operational Research, Elsevier, vol. 217(1), pages 141-148.
    • Handle: RePEc:eee:ejores:v:217:y:2012:i:1:p:141-148
    DOI: 10.1016/j.ejor.2011.08.020
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    References listed on IDEAS

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    Cited by:

    1. Wang, Honggang, 2017. "Multi-objective retrospective optimization using stochastic zigzag search," European Journal of Operational Research, Elsevier, vol. 263(3), pages 946-960.
    2. Honggang Wang, 2017. "Subspace dynamic‐simplex linear interpolation search for mixed‐integer black‐box optimization problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 305-322, June.

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