A two-stage adaptive robust optimization is developed for pre-disturbance scheduling in microgrids (MGs) handling uncertainties associated with electricity market prices, renewable generation, demand forecasts, and islanding events. The objective to produce a reliable optimal solution MG operation that minimizes operational costs the risk/failure In literature, uncertainty sets events cover ful...

Currently, there is a growing interesting in emotion recognition. Representation of emotional states very challenging issue. Considering the calculation cost and generalization capability for practical application, series features which contain common time frequency domain are extracted from physiological signals to represent different states. To reduce feature dimensionality improve recognitio...

this article presents the application of two algorithms: heuristic big bang-big crunch (hbb-bc) and a heuristic particle swarm ant colony optimization (hpsaco) to discrete optimization of reinforced concrete planar frames subject to combinations of gravity and lateral loads based on aci 318-08 code. the objective function is the total cost of the frame which includes the cost of concrete, formw...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs) which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of...

Data envelopment analysis is a nonparametric technique checking efficiency of DMUs using math programming. In conventional DEA, it has been assumed that the status of each measure is clearly known as either input or output. Kao and Hwang (2008) developed a data envelopment analysis (DEA) approach for measuring efficiency of decision processes which can be divided into two stages. The first stag...

In this study, a double-stage genetic optimization algorithm is proposed for portfolio selection. In the first stage, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the second stage, investment allocation in the selected good quality assets is optimized using a genetic algorithm based on Markowitz’s theory. Through the two-stage genetic optimization pr...

Logic synthesis has two stages of optimization: technologyindependent and technology-dependent. This paper surveys state-of-the-art methods for estimation and optimization of delays of logic circuits at technology-independent stage. Although at this stage we cannot completely predict nal delays after technology mapping, there exist reasonably accurate estimation techniques. Final delays can be ...

The traveling salesman problem (TSP) is one of the most important combinational optimization problems that have nowadays received much attention because of its practical applications in industrial and service problems. In this paper, a hybrid two-phase meta-heuristic algorithm called MACSGA used for solving the TSP is presented. At the first stage, the TSP is solved by the modified ant colony s...

We consider barrier problems associated with two and multistage stochastic convex optimization problems. We show that the barrier recourse functions at any stage form a selfconcordant family with respect to the barrier parameter. We also show that the complexity value of the first stage problem increases additively with the number of stages and scenarios. We use these results to propose a proto...