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A sequential randomized algorithm is developed for robust optimization which is to minimize a linear objective function subject to a parameter dependent convex constraint for all uncertain parameter values. The algorithm is realized as a probabilistic cutting plane technique based on maximum volume ellipsoid center, where candidates of the optimal value and of the optimal solution are sequentially...
This paper shows the versatility of the Particle Swarm Optimization, which attracts a lot of attention recently in the evolutionary computation area due to its empirical evidence of its superiority, in the area of continuous-time system identification. First, a method to identify (possibly nonlinear) continuous-time systems is shown, which uses the Particle Swarm Optimization to minimize the mean...
This paper gives an overview on probabilistic approach to robust optimization and chance constrained optimization. The problems are to minimize a linear objective function subject to a parameter dependent convex constraint, where a probability measure is introduced onto the parameter set. Two randomized techniques, the scenario optimization and the sequential optimization, are summarized, where characteristics...
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