Nonlinear Optimization is an intriguing area of study where mathematical theory, algorithms
and applications converge to calculate the optimal values of continuous functions. Within …

Many methods for solving optimal control problems, whether direct or indirect, rely upon
gradient information and therefore may converge to a local optimum. Global optimisation …

In this paper, we propose a parametric system identification approach for a class of
continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we …

In stratified sampling when strata weights are unknown double sampling technique may be
used to estimate them. At first a large simple random sample from the population without …

The prediction of thermochemical properties such as enthalpies of formation is of crucial
importance, both in research and industrial applications, especially for systems involving not …

We introduce a novel metaheuristic methodology to improve the initialization of a given
deterministic or stochastic optimization algorithm. Our objective is to improve the …

The paper addresses a random search optimization method for nonlinear problems with
continuous variables. The approach, called LJ-MM algorithm, deals with both unconstrained …

The modelling of a detailed kinetics mechanism in the liquid phase is still not well
understood, and the description of solvent effect on reaction rates remains a challenge …

This discussion paper for the SGO 2001 Workshop considers the process of investigating
stochastic global optimization algorithms. It outlines a general plan for the systematic study …

Most geophysical models include many parameters that are not fully determined by theory,
and can be “tuned” to improve the model's agreement with available data. We might attempt …