Retrospective optimization of mixed-integer stochastic systems using dynamic simplex linear interpolation |
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Authors: | Honggang Wang |
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Institution: | Department of Industrial & System Engineering, 96 Frelinghuysen Road, CoRE Building, Room 201, Piscataway, NJ 08854, United States |
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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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Keywords: | Simulation optimization Stochastic systems Mixed-integer optimization Sample-path approximation |
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