The economic load dispatch problem has been formulated as the minimization of total fuel cost needed to generate electricity in power plants. Due to the environmental issues that arise from the emissions of polluting gases produced by fossil-fueled electric power plants, it becomes the environ-mental/economic dispatch (EED) multi-objective optimization problem. EED problems have been most commonly solved using deterministic models (without considering uncertainties). In this context, a robust model to cope with uncertainties is necessary, because the deterministic model is not able to reflect some real condition in practical applications, since fuel cost and emission coefficients may be subjected to inaccuracies, for instance. In this paper, both deterministic and robust EED problem models are first formulated, and a Robust Evolutionary Optimization Algorithm - REOA is proposed. For the robust model, the worst case scenario is estimated, to find reliable solutions that are still feasible and have a good performance under the action of parametric uncertainties. Comparative studies are carried out to examine the effectiveness of the proposed approach. Simulation results are presented for the standard IEEE 30-bus system. The obtained results are satisfactory compared to the best solutions found in the literature.

The present work is dedicated to solving the problem of placing energy storage installations in electric power systems to improve the economic efficiency of their operation and maintenance with observing constraints on service quality. The initial problem is formulated and solved by mono-criteria optimization applying a Particle Swarm Optimization (PSO) algorithm. As the resolution power of mono-criteria optimization does not always permit one to obtain a convincing solution, the problem is reduced to the multicriteria decision making, applying additional criteria to reduce a decision uncertainty region. The analysis of the multicriteria problem is based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The paper results are illustrated by the two-stage solution of the problem of allocating an energy storage installation in a 30 bus IEEE test system. Keywords-Electrical Losses, Energy Storage, Operating Cost, Particle Swarm Optimization, Power Quality, TOPSIS.

This paper proposes the implementation of a multi-objective genetic algorithm (MOGA) on a low-end microcontroller, applying benchmark functions to evaluate its performance. A general-purpose microprocessor implementation of the same algorithm was used to compare CPU cycles spent for each generation on both platforms. The results show that although the microcontroller is slower than the microprocessor, it is fast enough to be used for solving optimization problems, and has lower cost and lower power consumption than the microprocessor platform.

Automated refactorings may change the program behavior. We propose an approach and its implementation called SafeRefactor for making program refactoring safer. We applied 10 Eclipse refactorings in a number of automatically generated programs, and used SafeRefactor to identify 50 bugs that lead to behavioral changes or compilation errors.

Uncertainties are commonly present in optimization systems, and when they are considered in the design stage, the problem usually is called a robust optimization problem. Robust optimization problems can be treated as noisy optimization problems, as worst case minimization problems, or by considering the mean and standard deviation values of the objective and constraint functions. The worst case scenario is preferred when the effects of the uncertainties on the nominal solution are critical to the application under consideration. Based on this worst case scenario, we developed the [I]RMOEA (Interval Robust Multi-Objective Evolutionary Algorithm), a hybrid method that combines interval analysis techniques to deal with the uncertainties in a deterministic way and a multi-objective evolutionary algorithm. We introduce [I]RMOEA and illustrate it on three robust test functions based on the ZDT problems. The results show that [I]RMOEA is an adequate way of tackling robust optimization problems with evolutionary techniques taking advantage of the interval analysis framework.