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The convergence speed is a major concern in using particle swarm optimization (PSO) in practice, especially when real-time computations are required. This paper proposes a method of parameter optimization for particle swarm optimization that has fast convergence speed in the stochastic sense. Using the theory of stochastic processes, a sufficient condition for the mean-square convergence of standard...
By using Lagrange duality methods, this paper studies the continuous-time mean-variance portfolio selection problem with uncertain exit time. Firstly, the original mean-variance problem is turned into a stochastic optimal control problem containing Lagrange multiplier. Secondly, the corresponding Hamilton- Jacobi-Bellman HJB equation is solved analytically. Thirdly, the efficient investment strategy...
In this paper, we are interested in the Robust Sensor Placement Problem (RSPP) in municipal water networks. As the contamination source and time are rather random and almost impossible to forecast, we aim to minimize the maximum population exposed over all contamination scenarios by placing a limited number of sensors into the network. We formulate a mixed-integer program model based on an absolute...
To date, optimization models of uncertain endogenous technological change models commonly add cost resulting from overestimating technological learning rates into an objective function with a subjective risk factor. This paper explores two risk-constrained stochastic optimization methods for dealing with uncertain technological learning with a simplified energy system model. The model assumes one...
We present the main elements for a decision support system for portfolio management in the Mexican market, including the financial investments consideration for a database, the uncertainty representation in scenario trees and the requirements for a portfolio optimization model. We used a stochastic programming approach to formulate the multistage optimization model, modified with new constraints and...
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