Parallel Randomized Algorithm for Chance Constrained Program
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by
Xun Shen, Jiancang Zhuang, Xingguo Zhang
2019
Abstract
Chance constrained program is computationally intractable due to the
existence of chance constraints, which are randomly disturbed and should be
satisfied with a probability. This paper proposes a two-layer randomized
algorithm to address chance constrained program. Randomized optimization is
applied to search the optimizer which satisfies chance constraints in a
framework of parallel algorithm. Firstly, multiple decision samples are
extracted uniformly in the decision domain without considering the chance
constraints. Then, in the second sampling layer, violation probabilities of all
the extracted decision samples are checked by extracting the disturbance
samples and calculating the corresponding violation probabilities. The decision
samples with violation probabilities higher than the required level are
discarded. The minimizer of the cost function among the remained feasible
decision samples are used to update optimizer iteratively. Numerical
simulations are implemented to validate the proposed method for non-convex
problems comparing with scenario approach. The proposed method exhibits better
robustness in finding probabilistic feasible optimizer.
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