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

► 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. We propose a family of retrospective...

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Vydáno v:European journal of operational research Ročník 217; číslo 1; s. 141 - 148
Hlavní autor: Wang, Honggang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 16.02.2012
Elsevier
Elsevier Sequoia S.A
Témata:
Algorithms
Applied sciences
Convergence
Decision theory. Utility theory
Dynamical systems
Dynamics
Exact sciences and technology
Integer programming
Interpolation
Mathematical analysis
Mathematical models
Mathematical programming
Mixed-integer optimization
Numerical analysis
Operational research and scientific management
Operational research. Management science
Optimization
Optimization algorithms
Sample-path approximation
Simulation optimization
Stochastic models
Stochastic systems
Studies
Sample-path approximation
Stochastic systems
Simulation optimization
Mixed-integer optimization
Linear interpolation
Evolutionary algorithm
Mixed integer programming
Dynamical system
Statistical simulation
Particle swarm optimization
Search algorithm
Random search
Uncertain system
Genetic algorithm
Swarm intelligence
Objective function
ISSN:0377-2217, 1872-6860
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Shrnutí:► 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. 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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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2011.08.020