This paper treats the solution of nonlinear optimization problems involving discrete decision variables, also known as generalized disjunctive programming (GDP) or mixed-integer nonlinear programming (MINLP) problems, that arise in process engineering. The key idea is to eliminate the discrete decision variables by adding a set of continuous variables and constraints that represent the discrete...

In this paper nonlinear optimization algorithms, namely the Gradient descent and the Gauss-Newton algorithms, are proposed for blind identification of MA models. A relationship between third and fourth order cumulants of the noisy system output and the MA parameters is exploited to build a set of nonlinear equations that is solved by means of the two nonlinear optimization algorithms above cite...

Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However, the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definiti...

In this paper we describe a new nonlinear estimator for filtering systems with nonlinear process and observation models, based on the optimization with RGO (Restricted Genetic Optimization). Simulation results are used to compare the performance of this method with EKF (Extended Kalman Filter), IEKF (Iterated Extended Kalman Filter), SNF (Second-order Nonlinear Filter), SIF (Single-stage Iterat...

Optimization is being increasing applied to engineering design problems throughout the world. iSIGHT is a generic engineering design environment that provides engineers with an optimization toolkit of leading optimization algorithms and an optimization advisor to solve their optimization needs. This paper focuses on the key role played by the toolkit’s genetic algorithm in providing a robust, g...

Robust nonlinear optimization is not as well developed as the linear case, and limited in the constraints and uncertainty sets it can handle. In this work we extend the scope of robust optimization by showing how to solve a large class of robust nonlinear optimization problems. The fascinating and appealing property of our approach is that any convex uncertainty set can be used. We give an expl...

Through mechanism analysis of simple genetic algorithm(SGA),every genetic operator can be considered as a linear transform. So some disadvantages of SGA may be solved if genetic operators are modified to nonlinear transforms. According to the above method, nonlinear genetic algorithm is introduced, and different nonlinear genetic operators with some probability are designed and applied to numer...

We consider optimization problems on Riemannian manifolds with equality and inequality constraints, which we call nonlinear (RNLO) problems. Although they have numerous applications, the existing studies them are limited especially in terms of algorithms. In this paper, propose sequential quadratic (RSQO) that uses a line-search technique an $\ell_{1}$ penalty function as extension standard SQO...

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle s...