Over the last few decades several methods have been proposed for handling functional constraints while solving optimization problems using evolutionary algorithms (EAs). However, the presence of equality constraints makes the feasible space very small compared to the entire search space. As a...

The paper proposes a primal-dual algorithm for solving an equality constrained minimization problem. The algorithm is a Newton-like method applied to a sequence of perturbed optimality systems that follow naturally from the quadratic penalty approach. This work is first motivated by the fact...

Feature relevance estimation is one of the most successful techniques used for improving the retrieval results of a content-based image retrieval (CBIR) system based on users' feedbacks. In this class of approaches, the weights of the feature elements (FEs) are adjusted based on the relevance...

This paper proposes a new method to find the capacity of a future wind farm regarding several limits of the grid system: voltage stability limits, thermal limits, voltage limits, load tap changing limitation and generator power output limitation. The method combines the optimization method and a...

Renewable energy sources (RES) are mainly used in the electrical sector. Electricity is not a storable commodity. Hence it is necessary to produce the requested quantity and distribute it through the system in such a way as to ensure that electricity supply and demand are always evenly balanced....

Impulsive control consideres so called interventions meaning immediate change of the state of the system; between intervention, the continuous time jump Markov process is uncontrollable, with “natural” jump intensities. Multicriteria control problem for such model is considered, and the...

We investigate the computational aspect of estimating discrete-choice games under incomplete information. In these games, multiple equilibria can exist. Also, different values of structural parameters can result in different numbers of equilibria. Consequently, under maximum-likelihood...

A novel hybrid evolutionary algorithm is developed based on the particle swarm optimization (PSO) and genetic algorithms (GAs). The PSO phase involves the enhancement of worst solutions by using the global-local best inertia weight and acceleration coefficients to increase the efficiency. In the...